# Standard library
import math
from itertools import combinations
from types import ModuleType
from typing import Any, Callable

# Third-party libraries
import anndata
import anndata as ad
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import plotly.graph_objs as go
import plotly.io as pio
import scanpy as sc
import seaborn as sns

# SciPy
from scipy.cluster.hierarchy import linkage, dendrogram
from scipy.stats import kurtosis, mode, skew

# Matplotlib
from matplotlib.patches import Patch
from matplotlib.ticker import FixedLocator, FixedFormatter

# scikit-learn
from sklearn.base import BaseEstimator, ClassifierMixin
from sklearn.cluster import AgglomerativeClustering, KMeans
from sklearn.decomposition import PCA
from sklearn.ensemble import RandomForestClassifier
from sklearn.exceptions import ConvergenceWarning
from sklearn.feature_selection import RFECV, SelectFromModel, SelectKBest
from sklearn.linear_model import LogisticRegression
from sklearn.manifold import trustworthiness
from sklearn.metrics import (
    accuracy_score,
    adjusted_rand_score,
    classification_report,
    confusion_matrix,
    normalized_mutual_info_score,
    silhouette_samples,
    silhouette_score,
)
from sklearn.model_selection import (
    GridSearchCV,
    RandomizedSearchCV,
    cross_val_score,
    learning_curve,
    train_test_split,
)
from sklearn.neural_network import MLPClassifier
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import LabelEncoder, StandardScaler
from sklearn.svm import LinearSVC, SVC

import warnings
warnings.resetwarnings()
warnings.filterwarnings("ignore")
warnings.filterwarnings("ignore", category = ConvergenceWarning)

# META DATA
ss_mcf7_meta = pd.read_csv("AILab2025/SmartSeq/MCF7_SmartS_MetaData.tsv",delimiter="\t",engine='python',index_col=0)
ss_mcf7_meta.head(5)

ss_mcf7_raw = pd.read_csv("AILab2025/SmartSeq/MCF7_SmartS_Unfiltered_Data.txt",delimiter=" ",engine='python',index_col=0)
gene_symbls = ss_mcf7_raw.index
print("Dataframe indexes: ", gene_symbls)
ss_mcf7_raw.shape

Dataframe indexes:  Index(['WASH7P', 'MIR6859-1', 'WASH9P', 'OR4F29', 'MTND1P23', 'MTND2P28',
       'MTCO1P12', 'MTCO2P12', 'MTATP8P1', 'MTATP6P1',
       ...
       'MT-TH', 'MT-TS2', 'MT-TL2', 'MT-ND5', 'MT-ND6', 'MT-TE', 'MT-CYB',
       'MT-TT', 'MT-TP', 'MAFIP'],
      dtype='object', length=22934)

(22934, 383)

# How much of each gene (row) is in each cell (column)
ss_mcf7_raw.head(5)

ss_mcf7_raw.describe()

# MISSING VALUES
ss_mcf7_raw.isnull().values.any()

np.False_

ss_mcf7_raw_small = ss_mcf7_raw.iloc[:, 150:220]
column_sums = ss_mcf7_raw_small.sum(axis=0)
column_sums_sorted = column_sums.sort_values(ascending=False)

sorted_labels = column_sums_sorted.index

clean_labels = sorted_labels.str.replace(r"output\.STAR\.", "", regex=True)
clean_labels = clean_labels.str.replace(r"_Aligned\.sortedByCoord\.out\.bam", "", regex=True)

colors = [
    'royalblue' if 'Hypo' in label else
    'seagreen' if 'Norm' in label else
    'gray'
    for label in clean_labels
]

plt.figure(figsize=(14,8))
plt.bar(clean_labels, column_sums_sorted.values, color=colors)
plt.xticks(rotation=90, fontsize=8)
plt.title('Total Number of Genes per Cell Type')
plt.xlabel('Cell Type')
plt.ylabel('Total Gene Count')
plt.grid(True, axis='y')
plt.tight_layout()
plt.show()

column_sums = ss_mcf7_raw.sum(axis=0)
column_sums_sorted = column_sums.sort_values(ascending=False)

sorted_labels = column_sums_sorted.index

clean_labels = sorted_labels.str.replace(r"output\.STAR\.", "", regex=True)
clean_labels = clean_labels.str.replace(r"_Aligned\.sortedByCoord\.out\.bam", "", regex=True)

# Extract letter from label (after first underscore)
first_letters = clean_labels.str.extract(r'_(\w)')[0]
print(first_letters)

from collections import defaultdict

# Initialize counters
group_counts = defaultdict(lambda: {'Hypo': 0, 'Norm': 0})

for idx, name in enumerate(clean_labels):
    # Extract the letter after first underscore
    letter = first_letters[idx]

    # Check condition
    if 'hypo' in name.lower():
        group_counts[letter]['Hypo'] += 1
    elif 'norm' in name.lower():
        group_counts[letter]['Norm'] += 1

print(group_counts)

0      C
1      B
2      C
3      A
4      A
      ..
378    E
379    H
380    D
381    G
382    H
Name: 0, Length: 383, dtype: object
defaultdict(<function <lambda> at 0x2bbcb47c0>, {'C': {'Hypo': 24, 'Norm': 24}, 'B': {'Hypo': 24, 'Norm': 24}, 'A': {'Hypo': 24, 'Norm': 24}, 'E': {'Hypo': 24, 'Norm': 24}, 'D': {'Hypo': 24, 'Norm': 24}, 'F': {'Hypo': 24, 'Norm': 24}, 'G': {'Hypo': 24, 'Norm': 24}, 'H': {'Hypo': 23, 'Norm': 24}})

Q1 = ss_mcf7_raw.quantile(0.25)
Q3 = ss_mcf7_raw.quantile(0.75)
IQR = Q3 - Q1

# Keep only the rows that have no ouliers
ss_mcf7_raw_noOut = ss_mcf7_raw[~((ss_mcf7_raw < (Q1 - 1.5 * IQR)) | (ss_mcf7_raw > (Q3 + 1.5 * IQR))).any(axis=1)]

ss_mcf7_raw_noOut.shape

(6435, 383)

# Create QC DataFrame
qc_ss_mcf7 = pd.DataFrame(index=ss_mcf7_raw.columns)

# Total counts
qc_ss_mcf7['total_counts'] = ss_mcf7_raw.sum(axis=0)
print("\nComputed total_counts per cell.")
print(qc_ss_mcf7['total_counts'].describe())

# Number of genes detected per cell
qc_ss_mcf7['n_genes'] = (ss_mcf7_raw > 0).sum(axis=0)
print("\nComputed n_genes per cell.")
print(qc_ss_mcf7['n_genes'].describe())

# Mitochondrial genes
mito_genes = [gene for gene in ss_mcf7_raw.index if gene.startswith("MT-") or gene.startswith("MT.")]
print(f"\nIdentified {len(mito_genes)} mitochondrial genes.")

# % Mitochondrial expression
qc_ss_mcf7['pct_mito'] = ss_mcf7_raw.loc[mito_genes].sum(axis=0) / qc_ss_mcf7['total_counts'] * 100
print("\nComputed percent mitochondrial gene expression per cell.")
print(qc_ss_mcf7['pct_mito'].describe())

# Percentage of Zeros per Sample
qc_ss_mcf7['percent_zeros'] = (ss_mcf7_raw == 0).sum(axis=0) / ss_mcf7_raw.shape[0] * 100

Computed total_counts per cell.
count    3.830000e+02
mean     9.946119e+05
std      5.503732e+05
min      1.000000e+00
25%      5.987505e+05
50%      1.129334e+06
75%      1.408638e+06
max      2.308057e+06
Name: total_counts, dtype: float64

Computed n_genes per cell.
count      383.000000
mean      9124.219321
std       2693.309249
min          1.000000
25%       8456.500000
50%       9907.000000
75%      10789.000000
max      12519.000000
Name: n_genes, dtype: float64

Identified 36 mitochondrial genes.

Computed percent mitochondrial gene expression per cell.
count    383.000000
mean       1.911659
std        2.355400
min        0.000000
25%        0.740893
50%        1.528072
75%        2.597771
max       31.033833
Name: pct_mito, dtype: float64

fig, axs = plt.subplots(1, 3, figsize=(15, 4))

axs[0].hist(qc_ss_mcf7['total_counts'], bins=30, color='gray')
axs[0].set_title("Total Counts per Sample")
axs[0].set_xlabel("Total Counts")

axs[1].hist(qc_ss_mcf7['n_genes'], bins=30, color='steelblue')
axs[1].set_title("Number of Genes per Sample")
axs[1].set_xlabel("Genes Detected")

axs[2].hist(qc_ss_mcf7['percent_zeros'], bins=30, color='darkred')
axs[2].set_title("% Zeros per Sample")
axs[2].set_xlabel("Percent Zeros")

plt.tight_layout()
plt.show()

fig, axes = plt.subplots(1, 3, figsize=(18, 6))

sns.violinplot(y=qc_ss_mcf7['total_counts'], ax=axes[0])
axes[0].set_title("Total Counts per Cell")

sns.violinplot(y=qc_ss_mcf7['n_genes'], ax=axes[1])
axes[1].set_title("Number of Genes per Cell")

sns.violinplot(y=qc_ss_mcf7['pct_mito'], ax=axes[2])
axes[2].set_title("Mitochondrial Gene %")

plt.tight_layout()
plt.show()

# QC Scatter Plot
adata = ad.AnnData(X=ss_mcf7_raw.T)

adata.obs['total_counts'] = qc_ss_mcf7['total_counts']
adata.obs['n_genes_by_counts'] = qc_ss_mcf7['n_genes']
adata.obs['pct_counts_mt'] = qc_ss_mcf7['pct_mito']

sc.pl.scatter(
    adata,
    x="total_counts",
    y="n_genes_by_counts",
    color="pct_counts_mt"
)

min_genes = 2_000  # Cells with very low gene counts (< 2000) should be filtered out
min_counts = 100_000  # Cells with extremely low counts may be low-quality
max_mito = 10  # A common threshold is 5%-10% to flag high-mito cells

high_quality_cells = qc_ss_mcf7[
    (qc_ss_mcf7['n_genes'] > min_genes) &
    (qc_ss_mcf7['total_counts'] > min_counts) &
    (qc_ss_mcf7['pct_mito'] < max_mito)
]

# Retain only the high-quality columns (cells)
ss_mcf7_raw_filt = ss_mcf7_raw[high_quality_cells.index]

print(f"Original: {ss_mcf7_raw.shape[1]} cells")
print(f"Filtered: {ss_mcf7_raw_filt.shape[1]} cells")

Original: 383 cells
Filtered: 337 cells

fig, axes = plt.subplots(1, 3, figsize=(18, 6))

sns.violinplot(y=high_quality_cells['total_counts'], ax=axes[0])
axes[0].set_title("Total Counts per Cell")

sns.violinplot(y=high_quality_cells['n_genes'], ax=axes[1])
axes[1].set_title("Number of Genes per Cell")

sns.violinplot(y=high_quality_cells['pct_mito'], ax=axes[2])
axes[2].set_title("Mitochondrial Gene %")

plt.tight_layout()
plt.show()

adata = ad.AnnData(X=ss_mcf7_raw_filt.T)

adata.obs['total_counts'] = high_quality_cells['total_counts']
adata.obs['n_genes_by_counts'] = high_quality_cells['n_genes']
adata.obs['pct_counts_mt'] = high_quality_cells['pct_mito']

sc.pl.violin(
    adata,
    ["total_counts", "n_genes_by_counts", "pct_counts_mt"],
    jitter=0.4,
    multi_panel=True
)

sc.pl.scatter(
    adata,
    x="total_counts",
    y="n_genes_by_counts",
    color="pct_counts_mt"
)

# Check original number of genes (rows)
original_rows = ss_mcf7_raw_filt.shape[0]

# Drop genes with all-zero expression
ss_mcf7_raw_filt = ss_mcf7_raw_filt.loc[~(ss_mcf7_raw_filt == 0).all(axis=1)]

# Check number of rows after dropping
remaining_rows = ss_mcf7_raw_filt.shape[0]

# Compute how many were dropped
dropped_rows = original_rows - remaining_rows
print(f"Number of all-zero rows dropped: {dropped_rows}")

Number of all-zero rows dropped: 36

duplicate_rows = ss_mcf7_raw_filt[ss_mcf7_raw_filt.duplicated(keep=False)]
print("number of duplicate rows: ", duplicate_rows.shape[0])
print("Rows before:", ss_mcf7_raw_filt.shape[0])
ss_mcf7_raw_filt = ss_mcf7_raw_filt.drop_duplicates()
print("Rows after :", ss_mcf7_raw_filt.shape[0])

number of duplicate rows:  98
Rows before: 22898
Rows after : 22843

zero_cols = (ss_mcf7_raw_filt == 0).all(axis=0)
print(f"Number of all-zero columns: {zero_cols.sum()}")

Number of all-zero columns: 0

from scipy.stats import kurtosis, skew
colN = np.shape(ss_mcf7_raw_filt)[1]
colN
df_skew_cells = []
cnames = ss_mcf7_raw_filt.columns

for i in range(colN) :     
     v_df = ss_mcf7_raw_filt[cnames[i]]
     df_skew_cells += [skew(v_df)]   
  #  df_skew_cells += [df[cnames[i]].skew()]
df_skew_cells
sns.histplot(df_skew_cells,bins=100)
plt.xlabel('Skewness of single cells expression profiles - ss_mcf7_raw_filt')

Text(0.5, 0, 'Skewness of single cells expression profiles - ss_mcf7_raw_filt')

df_kurt_cells = []
for i in range(colN) :     
     v_df = ss_mcf7_raw_filt[cnames[i]]
     df_kurt_cells += [kurtosis(v_df)]   
df_kurt_cells
sns.histplot(df_kurt_cells,bins=100)
plt.xlabel('Kurtosis of single cells expression profiles - ss_mcf7_raw_filt')

Text(0.5, 0, 'Kurtosis of single cells expression profiles - ss_mcf7_raw_filt')

# DATA TRANSFORMATION
ss_mcf7_raw_filt_log = np.log2(ss_mcf7_raw_filt + 1)  # genes × cells
ss_mcf7_raw_filt_T = ss_mcf7_raw_filt.T
ss_mcf7_raw_filt_T_log = np.log2(ss_mcf7_raw_filt_T + 1)  # cells × genes (transpose necessary for skew() and kurtosis())

# Skeweness and Kurtosis should be fixed now
print("Before data transformation:", skew(ss_mcf7_raw_filt.T.values.flatten()), kurtosis(ss_mcf7_raw_filt.T.values.flatten()))
print("After data transformation:", skew(ss_mcf7_raw_filt_T_log.values.flatten()), kurtosis(ss_mcf7_raw_filt_T_log.values.flatten()))

Before data transformation: 85.61749272454507 11944.74886564649
After data transformation: 0.9824438044182301 -0.3223454127671732

colN = ss_mcf7_raw_filt_log.shape[1]
cnames = ss_mcf7_raw_filt_log.columns

# Compute skeweness for each cell
df_skew_cells_log = []
for i in range(colN):
    v_df = ss_mcf7_raw_filt_log[cnames[i]]
    df_skew_cells_log.append(skew(v_df))

# Plot histogram
sns.histplot(df_skew_cells_log, bins=100)
plt.xlabel('Skewness of single cells expression profiles (log2 transformed)')
plt.title('Distribution of Skewness (log2-transformed MCF7)')
plt.tight_layout()
plt.show()

# Compute kurtosis for each cell
df_kurt_cells_log = []
for i in range(colN):     
    v_df = ss_mcf7_raw_filt_log[cnames[i]]
    df_kurt_cells_log.append(kurtosis(v_df))

# Plot histogram of kurtosis
sns.histplot(df_kurt_cells_log, bins=100)
plt.xlabel('Kurtosis of single cells expression profiles (log2-transformed)')
plt.title('Distribution of Kurtosis - log2(ss_mcf7_raw_filt + 1)')
plt.tight_layout()
plt.show()

# 1. Transpose the DataFrame so that rows = samples, columns = genes
ss_mcf7_raw_filt_T = ss_mcf7_raw_filt.T  # transpose necessary for pca.fit_transform()

# 2. Log-transform if data isn't already normalized
ss_mcf7_raw_filt_T_log = np.log2(ss_mcf7_raw_filt_T + 1)

# 3. Generate colors based on column/sample names
colors = ['royalblue' if 'hypo' in name.lower() else 'seagreen' for name in ss_mcf7_raw_filt_T_log.index]

# 4. Run PCA with 2 components
pca = PCA(n_components=2)
pc = pca.fit_transform(ss_mcf7_raw_filt_T_log)

# 5. Plot
plt.figure(figsize=(8,6))
plt.scatter(pc[:,0], pc[:,1], c=colors)
plt.xlabel(f'PC1 ({pca.explained_variance_ratio_[0]*100:.1f}%)')
plt.ylabel(f'PC2 ({pca.explained_variance_ratio_[1]*100:.1f}%)')
plt.title("PCA of Samples (Colored by Condition)")
plt.grid(True)

legend_elements = [
    Patch(facecolor='seagreen', label='Normoxia'),
    Patch(facecolor='royalblue', label='Hypoxia')
]
plt.legend(handles=legend_elements, title='Condition')

plt.tight_layout()
plt.show()

# Run PCA with 3 components
pca = PCA(n_components=3)
pc = pca.fit_transform(ss_mcf7_raw_filt_T_log)

pio.renderers.default = 'browser'

# Extract labels from sample names
labels = ['Hypo' if 'hypo' in name.lower() else 'Norm' for name in ss_mcf7_raw_filt_T_log.index]

# Get indices for each condition
hypo_idx = np.array(labels) == 'Hypo'
norm_idx = np.array(labels) == 'Norm'

fig = go.Figure()

# Hypo samples
fig.add_trace(go.Scatter3d(
    x=pc[hypo_idx, 0],
    y=pc[hypo_idx, 1],
    z=pc[hypo_idx, 2],
    mode='markers',
    name='Hypo',
    marker=dict(color='royalblue', size=6),
    text=ss_mcf7_raw_filt_T_log.index[hypo_idx],
    hoverinfo='text'
))

# Norm samples
fig.add_trace(go.Scatter3d(
    x=pc[norm_idx, 0],
    y=pc[norm_idx, 1],
    z=pc[norm_idx, 2],
    mode='markers',
    name='Norm',
    marker=dict(color='seagreen', size=6),
    text=ss_mcf7_raw_filt_T_log.index[norm_idx],
    hoverinfo='text'
))

# Set layout
fig.update_layout(
    scene=dict(
        xaxis_title=f'PC1 ({pca.explained_variance_ratio_[0]*100:.1f}%)',
        yaxis_title=f'PC2 ({pca.explained_variance_ratio_[1]*100:.1f}%)',
        zaxis_title=f'PC3 ({pca.explained_variance_ratio_[2]*100:.1f}%)'
    ),
    title='3D PCA of Samples (Interactive)',
    margin=dict(l=0, r=0, b=0, t=40)
)

fig.show()

labels = [1 if 'hypo' in name.lower() else 0 for name in ss_mcf7_raw_filt_T_log.index]

clf = LogisticRegression()
clf.fit(pc, labels)

# Extract coefficients (normal vector to the plane)
w = clf.coef_[0]  # [w1, w2, w3]
b = clf.intercept_[0]

# Create grid to cover the PCA space
x_range = np.linspace(pc[:, 0].min(), pc[:, 0].max(), 10)
y_range = np.linspace(pc[:, 1].min(), pc[:, 1].max(), 10)
xx, yy = np.meshgrid(x_range, y_range)

# Compute corresponding z for the plane
zz = (-w[0] * xx - w[1] * yy - b) / w[2]

# Compute decision values for all points
decision_values = np.dot(pc, w) + b

# Predicted labels: 1 if value > 0 (Hypo), else 0 (Norm)
predicted_labels = (decision_values > 0).astype(int)

labels = np.array([1 if 'hypo' in name.lower() else 0 for name in ss_mcf7_raw_filt_T_log.index])

accuracy = (predicted_labels == labels).mean()
print(f"Accuracy of plane: {accuracy * 100:.2f}%")

Accuracy of plane: 99.41%

ss_hcc_raw = pd.read_csv("AILab2025/SmartSeq/HCC1806_SmartS_Unfiltered_Data.txt",delimiter=" ",engine='python',index_col=0)
gene_symbls = ss_hcc_raw.index
print("Dataframe indexes: ", gene_symbls)
ss_hcc_raw.shape

Dataframe indexes:  Index(['WASH7P', 'CICP27', 'DDX11L17', 'WASH9P', 'OR4F29', 'MTND1P23',
       'MTND2P28', 'MTCO1P12', 'MTCO2P12', 'MTATP8P1',
       ...
       'MT-TH', 'MT-TS2', 'MT-TL2', 'MT-ND5', 'MT-ND6', 'MT-TE', 'MT-CYB',
       'MT-TT', 'MT-TP', 'MAFIP'],
      dtype='object', length=23396)

(23396, 243)

ss_hcc_raw.head(5)

ss_hcc_raw.describe()

# MISSING VALUES
ss_hcc_raw.isnull().values.any()

np.False_

ss_hcc_raw_small = ss_hcc_raw.iloc[:, 150:220]
column_sums = ss_hcc_raw_small.sum(axis=0)
column_sums_sorted = column_sums.sort_values(ascending=False)

sorted_labels = column_sums_sorted.index

clean_labels = sorted_labels.str.replace(r"output\.STAR\.", "", regex=True)
clean_labels = clean_labels.str.replace(r"_Aligned\.sortedByCoord\.out\.bam", "", regex=True)

colors = [
    'royalblue' if 'Hypo' in label else
    'seagreen' if 'Norm' in label else
    'gray'
    for label in clean_labels
]

plt.figure(figsize=(14,8))
plt.bar(clean_labels, column_sums_sorted.values, color=colors)
plt.xticks(rotation=90, fontsize=8)
plt.title('Total Number of Genes per Cell Type')
plt.xlabel('Cell Type')
plt.ylabel('Total Gene Count')
plt.grid(True, axis='y')
plt.tight_layout()
plt.show()

Q1 = ss_hcc_raw.quantile(0.25)
Q3 = ss_hcc_raw.quantile(0.75)
IQR = Q3 - Q1

# Keep only the rows that have no ouliers
ss_hcc_raw_noOut = ss_hcc_raw[~((ss_hcc_raw < (Q1 - 1.5 * IQR)) | (ss_hcc_raw > (Q3 + 1.5 * IQR))).any(axis=1)]

ss_hcc_raw_noOut.shape

(10815, 243)

# Create QC DataFrame
qc_ss_hcc = pd.DataFrame(index=ss_hcc_raw.columns)

# Total counts
qc_ss_hcc['total_counts'] = ss_hcc_raw.sum(axis=0)
print("\nComputed total_counts per cell.")
print(qc_ss_hcc['total_counts'].describe())

# Number of genes detected per cell
qc_ss_hcc['n_genes'] = (ss_hcc_raw > 0).sum(axis=0)
print("\nComputed n_genes per cell.")
print(qc_ss_hcc['n_genes'].describe())

# Mitochondrial genes
mito_genes = [gene for gene in ss_hcc_raw.index if gene.startswith("MT-") or gene.startswith("MT.")]
print(f"\nIdentified {len(mito_genes)} mitochondrial genes.")

# % Mitochondrial expression
qc_ss_hcc['pct_mito'] = ss_hcc_raw.loc[mito_genes].sum(axis=0) / qc_ss_hcc['total_counts'] * 100
print("\nComputed percent mitochondrial gene expression per cell.")
print(qc_ss_hcc['pct_mito'].describe())

# Percentage of Zeros per Sample
qc_ss_hcc['percent_zeros'] = (ss_hcc_raw == 0).sum(axis=0) / ss_hcc_raw.shape[0] * 100

Computed total_counts per cell.
count    2.430000e+02
mean     2.012306e+06
std      1.171858e+06
min      1.140000e+02
25%      9.910625e+05
50%      2.067645e+06
75%      2.925182e+06
max      5.758132e+06
Name: total_counts, dtype: float64

Computed n_genes per cell.
count      243.000000
mean     10330.358025
std       2260.259356
min         35.000000
25%      10117.000000
50%      10831.000000
75%      11409.000000
max      13986.000000
Name: n_genes, dtype: float64

Identified 36 mitochondrial genes.

Computed percent mitochondrial gene expression per cell.
count    243.000000
mean       2.197282
std        3.173782
min        0.000000
25%        1.462458
50%        1.840945
75%        2.468950
max       49.215686
Name: pct_mito, dtype: float64

fig, axs = plt.subplots(1, 3, figsize=(15, 4))

axs[0].hist(qc_ss_hcc['total_counts'], bins=30, color='gray')
axs[0].set_title("Total Counts per Sample")
axs[0].set_xlabel("Total Counts")

axs[1].hist(qc_ss_hcc['n_genes'], bins=30, color='steelblue')
axs[1].set_title("Number of Genes per Sample")
axs[1].set_xlabel("Genes Detected")

axs[2].hist(qc_ss_hcc['percent_zeros'], bins=30, color='darkred')
axs[2].set_title("% Zeros per Sample")
axs[2].set_xlabel("Percent Zeros")

plt.tight_layout()
plt.show()

fig, axes = plt.subplots(1, 3, figsize=(18, 6))

sns.violinplot(y=qc_ss_hcc['total_counts'], ax=axes[0])
axes[0].set_title("Total Counts per Cell")

sns.violinplot(y=qc_ss_hcc['n_genes'], ax=axes[1])
axes[1].set_title("Number of Genes per Cell")

sns.violinplot(y=qc_ss_hcc['pct_mito'], ax=axes[2])
axes[2].set_title("Mitochondrial Gene %")

plt.tight_layout()
plt.show()

adata = ad.AnnData(X=ss_hcc_raw.T)

adata.obs['total_counts'] = qc_ss_hcc['total_counts']
adata.obs['n_genes_by_counts'] = qc_ss_hcc['n_genes']
adata.obs['pct_counts_mt'] = qc_ss_hcc['pct_mito']

sc.pl.scatter(
    adata,
    x="total_counts",
    y="n_genes_by_counts",
    color="pct_counts_mt"
)

min_genes = 2_000  # Cells with very low gene counts (< 1000) should be filtered out
min_counts = 100_000  # Cells with extremely low counts may be low-quality
max_mito = 10  # A common threshold is 5%-10% to flag high-mito cells

high_quality_cells = qc_ss_hcc[
    (qc_ss_hcc['n_genes'] > min_genes) &
    (qc_ss_hcc['total_counts'] > min_counts) &
    (qc_ss_hcc['pct_mito'] < max_mito)
]

# Retain only the high-quality columns (cells)
ss_hcc_raw_filt = ss_hcc_raw[high_quality_cells.index]

print(f"Original: {ss_hcc_raw.shape[1]} cells")
print(f"Filtered: {ss_hcc_raw_filt.shape[1]} cells")

Original: 243 cells
Filtered: 233 cells

fig, axes = plt.subplots(1, 3, figsize=(18, 6))

sns.violinplot(y=high_quality_cells['total_counts'], ax=axes[0])
axes[0].set_title("Total Counts per Cell")

sns.violinplot(y=high_quality_cells['n_genes'], ax=axes[1])
axes[1].set_title("Number of Genes per Cell")

sns.violinplot(y=high_quality_cells['pct_mito'], ax=axes[2])
axes[2].set_title("Mitochondrial Gene %")

plt.tight_layout()
plt.show()

adata = ad.AnnData(X=ss_hcc_raw_filt.T)

adata.obs['total_counts'] = high_quality_cells['total_counts']
adata.obs['n_genes_by_counts'] = high_quality_cells['n_genes']
adata.obs['pct_counts_mt'] = high_quality_cells['pct_mito']

sc.pl.violin(
    adata,
    ["total_counts", "n_genes_by_counts", "pct_counts_mt"],
    jitter=0.4,
    multi_panel=True
)

sc.pl.scatter(
    adata,
    x="total_counts",
    y="n_genes_by_counts",
    color="pct_counts_mt"
)

# Check original number of genes (rows)
original_rows = ss_hcc_raw_filt.shape[0]

# Drop genes with all-zero expression
ss_hcc_raw_filt = ss_hcc_raw_filt.loc[~(ss_hcc_raw_filt == 0).all(axis=1)]

# Check number of rows after dropping
remaining_rows = ss_hcc_raw_filt.shape[0]

# Compute how many were dropped
dropped_rows = original_rows - remaining_rows
print(f"Number of all-zero rows dropped: {dropped_rows}")

Number of all-zero rows dropped: 0

duplicate_rows = ss_hcc_raw_filt[ss_hcc_raw_filt.duplicated(keep=False)]
print("number of duplicate rows: ", duplicate_rows.shape[0])
print("Rows before:", ss_hcc_raw_filt.shape[0])
ss_hcc_raw_filt = ss_hcc_raw_filt.drop_duplicates()
print("Rows after :", ss_hcc_raw_filt.shape[0])

number of duplicate rows:  92
Rows before: 23396
Rows after : 23339

# DATA TRANSFORMATION
ss_hcc_raw_filt_log = np.log2(ss_hcc_raw_filt + 1)  # genes × cells
ss_hcc_raw_filt_T = ss_hcc_raw_filt.T
ss_hcc_raw_filt_T_log = np.log2(ss_hcc_raw_filt_T + 1)  # cells × genes

print("Before data transformation:", skew(ss_hcc_raw_filt.T.values.flatten()), kurtosis(ss_hcc_raw_filt.T.values.flatten()))
print("After data transformation:", skew(ss_hcc_raw_filt_T_log.values.flatten()), kurtosis(ss_hcc_raw_filt_T_log.values.flatten()))

Before data transformation: 66.87855806046676 10509.765195762657
After data transformation: 0.8280895490120331 -0.7178446753891188

# 1. Transpose the DataFrame so that rows = samples, columns = genes
ss_hcc_raw_filt_T = ss_hcc_raw_filt.T  # transpose necessary for pca.fit_transform()

# 2. Log-transform if data isn't already normalized
ss_hcc_raw_filt_T_log = np.log2(ss_hcc_raw_filt_T + 1)

# 3. Generate colors based on column/sample names
colors = ['royalblue' if 'hypo' in name.lower() else 'seagreen' for name in ss_hcc_raw_filt_T_log.index]

# 4. Run PCA with 2 components
pca = PCA(n_components=2)
pc = pca.fit_transform(ss_hcc_raw_filt_T_log)

# 5. Plot
plt.figure(figsize=(8,6))
plt.scatter(pc[:,0], pc[:,1], c=colors)
plt.xlabel(f'PC1 ({pca.explained_variance_ratio_[0]*100:.1f}%)')
plt.ylabel(f'PC2 ({pca.explained_variance_ratio_[1]*100:.1f}%)')
plt.title("PCA of Samples (Colored by Condition)")
plt.grid(True)
plt.tight_layout()
plt.show()

# Run PCA with 3 components
pca = PCA(n_components=3)
pc = pca.fit_transform(ss_hcc_raw_filt_T_log)

pio.renderers.default = 'browser'

# Extract labels from sample names
labels = ['Hypo' if 'hypo' in name.lower() else 'Norm' for name in ss_hcc_raw_filt_T_log.index]

# Get indices for each condition
hypo_idx = np.array(labels) == 'Hypo'
norm_idx = np.array(labels) == 'Norm'

fig = go.Figure()

# Hypo samples
fig.add_trace(go.Scatter3d(
    x=pc[hypo_idx, 0],
    y=pc[hypo_idx, 1],
    z=pc[hypo_idx, 2],
    mode='markers',
    name='Hypo',
    marker=dict(color='royalblue', size=6),
    text=ss_hcc_raw_filt_T_log.index[hypo_idx],
    hoverinfo='text'
))

# Norm samples
fig.add_trace(go.Scatter3d(
    x=pc[norm_idx, 0],
    y=pc[norm_idx, 1],
    z=pc[norm_idx, 2],
    mode='markers',
    name='Norm',
    marker=dict(color='seagreen', size=6),
    text=ss_hcc_raw_filt_T_log.index[norm_idx],
    hoverinfo='text'
))

# Set layout
fig.update_layout(
    scene=dict(
        xaxis_title=f'PC1 ({pca.explained_variance_ratio_[0]*100:.1f}%)',
        yaxis_title=f'PC2 ({pca.explained_variance_ratio_[1]*100:.1f}%)',
        zaxis_title=f'PC3 ({pca.explained_variance_ratio_[2]*100:.1f}%)'
    ),
    title='3D PCA of Samples (Interactive)',
    margin=dict(l=0, r=0, b=0, t=40)
)

fig.show()

labels = [1 if 'hypo' in name.lower() else 0 for name in ss_hcc_raw_filt_T_log.index]

clf = LogisticRegression()
clf.fit(pc, labels)

# Extract coefficients (normal vector to the plane)
w = clf.coef_[0]  # [w1, w2, w3]
b = clf.intercept_[0]

# Create grid to cover the PCA space
x_range = np.linspace(pc[:, 0].min(), pc[:, 0].max(), 10)
y_range = np.linspace(pc[:, 1].min(), pc[:, 1].max(), 10)
xx, yy = np.meshgrid(x_range, y_range)

# Compute corresponding z for the plane
zz = (-w[0] * xx - w[1] * yy - b) / w[2]

# Compute decision values for all points
decision_values = np.dot(pc, w) + b

# Predicted labels: 1 if value > 0 (Hypo), else 0 (Norm)
predicted_labels = (decision_values > 0).astype(int)

labels = np.array([1 if 'hypo' in name.lower() else 0 for name in ss_hcc_raw_filt_T_log.index])

accuracy = (predicted_labels == labels).mean()
print(f"Accuracy of plane: {accuracy * 100:.2f}%")

Accuracy of plane: 90.13%

ss_mcf7_filt = pd.read_csv("AILab2025/SmartSeq/MCF7_SmartS_Filtered_Data.txt",delimiter=" ",engine='python',index_col=0)
ss_mcf7_norm = pd.read_csv("AILab2025/SmartSeq/MCF7_SmartS_Filtered_Normalised_3000_Data_train.txt",delimiter=" ",engine='python',index_col=0)

ss_hcc_filt = pd.read_csv("AILab2025/SmartSeq/HCC1806_SmartS_Filtered_Data.txt",delimiter=" ",engine='python',index_col=0)
ss_hcc_norm = pd.read_csv("AILab2025/SmartSeq/HCC1806_SmartS_Filtered_Normalised_3000_Data_train.txt",delimiter=" ",engine='python',index_col=0)

ss_mcf7_filt.describe()

ss_hcc_filt.describe()

ss_mcf7_norm.describe()

ss_mcf7_norm.head(5)

print("MCF7 raw:", ss_mcf7_raw.shape)
print("Filtered shape:", ss_mcf7_filt.shape)
print("Normalised shape:", ss_mcf7_norm.shape)

print("\nHCC1806 raw:", ss_hcc_raw.shape)
print("Filtered shape:", ss_hcc_filt.shape)
print("Normalised shape:", ss_hcc_norm.shape)

MCF7 raw: (22934, 383)
Filtered shape: (18945, 313)
Normalised shape: (3000, 250)

HCC1806 raw: (23396, 243)
Filtered shape: (19503, 227)
Normalised shape: (3000, 182)

ss_mcf7_raw_filt.shape

(22843, 337)

genes_raw = set(ss_mcf7_raw.index)
genes_filtered = set(ss_mcf7_filt.index)

dropped_genes = genes_raw - genes_filtered
print(f"Genes dropped: {len(dropped_genes)}")

Genes dropped: 3989

# Check expression stats for dropped genes
dropped_stats = ss_mcf7_raw.loc[list(dropped_genes)].sum(axis=1).describe()
print(dropped_stats)

count    3989.000000
mean       19.475307
std        43.194078
min         2.000000
25%         4.000000
50%         8.000000
75%        20.000000
max      1530.000000
dtype: float64

# Genes that are expressed in more than 5 cells
ss_mcf7_raw_genes_mask = (ss_mcf7_raw > 0).sum(axis=1) > 5  # higher threshold results in less than 18945 genes remaining
ss_mcf7_raw_gene_set = set(ss_mcf7_raw.index[ss_mcf7_raw_genes_mask])

print(f"Genes passing our threshold: {len(ss_mcf7_raw_gene_set)}")

Genes passing our threshold: 19182

ss_mcf7_raw_gene_set = ss_mcf7_raw.index[ss_mcf7_raw_genes_mask]

ss_mcf7_filt_gene_set = ss_mcf7_filt.index

overlap = ss_mcf7_raw_gene_set.intersection(ss_mcf7_filt_gene_set)
print(f"Overlap: {len(overlap)} / {len(ss_mcf7_filt_gene_set)}")

Overlap: 18945 / 18945

# Convert Indexes to sets
ss_mcf7_raw_gene_set = set(ss_mcf7_raw_gene_set)
ss_mcf7_filt_gene_set = set(ss_mcf7_filt_gene_set)

# Find extra genes (present in the threshold but not in the filtered set)
extra_genes = ss_mcf7_raw_gene_set - ss_mcf7_filt_gene_set
extra_genes_list = list(extra_genes)
ss_mcf7_raw.loc[extra_genes_list].mean(axis=1).describe()

count    237.000000
mean       0.125183
std        0.160211
min        0.015666
25%        0.033943
50%        0.078329
75%        0.156658
max        1.360313
dtype: float64

# Expression counts for the remaining 237 genes
ss_mcf7_raw.loc[extra_genes_list].sum(axis=1).describe()

count    237.000000
mean      47.945148
std       61.360780
min        6.000000
25%       13.000000
50%       30.000000
75%       60.000000
max      521.000000
dtype: float64

subset = ss_mcf7_raw.loc[extra_genes_list]
gene_means = subset.mean(axis=1)
gene_vars = subset.var(axis=1)
gene_dispersion = gene_vars / gene_means.replace(0, np.nan)

print("\nVariance:")
print(gene_vars.describe())

print("\nDispersion (var / mean):")
print(gene_dispersion.describe())

Variance:
count    237.000000
mean       6.859440
std       35.000200
min        0.015461
25%        0.105792
50%        0.637089
75%        2.725698
max      432.822714
dtype: float64

Dispersion (var / mean):
count    237.000000
mean      17.180863
std       31.133525
min        0.984293
25%        2.979058
50%        8.507272
75%       18.379411
max      318.178694
dtype: float64

extra_genes_series = pd.Series(extra_genes_list)

# Check for known non-informative categories
is_mito = extra_genes_series.str.startswith("MT-")
is_ribo = extra_genes_series.str.startswith("RPL") | extra_genes_series.str.startswith("RPS")
is_pseudo = extra_genes_series.str.contains("-P")
is_mirna = extra_genes_series.str.contains("MIR")
is_linc = extra_genes_series.str.contains("LINC")

print(f"Mitochondrial: {is_mito.sum()}")
print(f"Ribosomal: {is_ribo.sum()}")
print(f"Pseudogenes (-P): {is_pseudo.sum()}")
print(f"miRNAs (MIR): {is_mirna.sum()}")
print(f"LINC: {is_linc.sum()}")

Mitochondrial: 0
Ribosomal: 13
Pseudogenes (-P): 0
miRNAs (MIR): 8
LINC: 13

print(extra_genes_list)

['SLC9C2', 'FEZF1-AS1', 'CALD1', 'KLRG1', 'ASAP1-IT2', 'GOLGA8R', 'LINC02137', 'MIR3143', 'DDR1-DT', 'LINC00661', 'PARP4P2', 'KCNC1', 'LINC00964', 'RPL12P33', 'CYP2F1', 'SLC34A3', 'TRAV27', 'SPOCK2', 'HSPB6', 'SNAP91', 'AMDHD1', 'FST', 'HMGN2P23', 'OR8B7P', 'HNRNPA1P35', 'MRRFP1', 'FNDC8', 'LINC02035', 'TNNC1', 'ARL9', 'ARHGEF18-AS1', 'SNTG1', 'XAGE2', 'SH3TC1', 'TAS2R43', 'TRPM3', 'LRRC43', 'P3H3', 'TLR9', 'IQGAP2', 'PRB2', 'STAB2', 'MAPK11', 'RPL7P36', 'ADM-DT', 'SOHLH2', 'QRICH2', 'C17orf50', 'TUSC8', 'LRIG3-DT', 'CELF3', 'RP1L1', 'IPCEF1', 'A2ML1', 'MSX2P1', 'HMGA1P3', 'PRSS30P', 'CD70', 'LINC00310', 'ARAP3', 'DDX10P2', 'MYO1A', 'AJAP1', 'RNA5SP18', 'MIR7161', 'SNORD3B-2', 'NDUFA3P1', 'CUX2', 'PKD1P2', 'HSPE1P6', 'HNRNPA3P9', 'RPL5P5', 'HMX3', 'CPB2-AS1', 'MXRA8', 'CELF4', 'MCTP2', 'RNA5SP392', 'PDPN', 'RPL23AP73', 'HSPE1P7', 'PPIAP51', 'SLC7A9', 'SNORA68B', 'CA5A', 'DNAJC8P1', 'PPARGC1A', 'OR2A7', 'LINC02169', 'TYRO3P', 'RPL32P2', 'HLA-S', 'RNA5SP440', 'HTR2B', 'AMPH', 'RALBP1P1', 'MIR4449', 'PSAT1', 'RNA5SP477', 'TRMT1P1', 'CCBE1', 'TAL2', 'UBE2CP2', 'LRAT', 'KLF1', 'OR7E7P', 'OR8B5P', 'MAMDC2-AS1', 'WIPF3', 'ABHD12B', 'GPR150', 'CHRNG', 'SLC7A2-IT1', 'MIR3188', 'ITGA1', 'MIR503', 'CCN3', 'LINC02895', 'LINC00572', 'RN7SL688P', 'HHIP', 'RPL23AP10', 'IGLV1-51', 'VAV1', 'SYT2', 'MIR2861', 'ADIPOQ', 'KRT37', 'CACNA1A', 'RNVU1-21', 'BACH1-AS1', 'KIF26B', 'RNU1-1', 'AQP8', 'RPS7P11', 'HOXB-AS1', 'C3orf35', 'ALOX15P1', 'NUTM2F', 'PLAT', 'IGF2-AS', 'NAPSB', 'TEX38', 'RPL7P52', 'LRRC15', 'ARG1', 'TSPEAR-AS2', 'FER1L6', 'PTMAP15', 'SEC14L5', 'TMEM47', 'LINC01224', 'WWC2-AS2', 'RRAD', 'DHX58', 'RPL21P34', 'NR1I2', 'LINC01863', 'PNMA8B', 'KLHL6', 'LCN2', 'DNAH12', 'CHMP4BP1', 'PIWIL2', 'DDC', 'TCF7L1-IT1', 'COLGALT2', 'HLA-DQA2', 'NOVA2', 'CPLX3', 'PCDHB5', 'CABP1', 'NRXN3', 'RBBP4P4', 'TRIM72', 'VTRNA1-1', 'ENPP3', 'SNORD98', 'MIR4271', 'TIGD4', 'OR4F21', 'HMGB1P26', 'EYS', 'VWA2', 'ITIH4', 'BMS1P3', 'FAM71F2', 'LINC02009', 'GAPDHP39', 'CES3', 'ANKLE1', 'PADI1', 'MRPL23-AS1', 'PEG10', 'THSD4-AS1', 'SHD', 'FTH1P1', 'PTCHD4', 'RN7SL605P', 'BATF2', 'AOC1', 'ABCC13', 'TSPEAR', 'MIR3175', 'TRIM17', 'RBP5', 'ELMOD1', 'LINC02014', 'BANK1', 'RPL26P6', 'RPL8P1', 'TMEM88', 'LARP1P1', 'H3P42', 'RPL4P2', 'TIMP4', 'FER1L6-AS2', 'FAM135A', 'ITGB7', 'RPL21P131', 'EPSTI1', 'IGLL3P', 'DPP3P1', 'GZMM', 'RN7SKP36', 'VILL', 'LINC01456', 'SPATA46', 'ANGPT4', 'S100A7', 'A2M', 'SNCAIP', 'DAND5', 'NFYBP1', 'EPO', 'SERPINA4', 'GOSR2-DT']

duplicate_rows = ss_mcf7_raw[ss_mcf7_raw.duplicated(keep=False)]
print("number of duplicate rows: ", duplicate_rows.shape[0])

# Convert to sets
duplicate_gene_set = set(duplicate_rows.index)
extra_removed_set = set(extra_genes_list)

# Intersect to find which of the 237 removed genes were duplicates
dup_overlap = extra_removed_set.intersection(duplicate_gene_set)

print(f"Removed genes that were also duplicates: {len(dup_overlap)}")
print("Example overlapping genes:", list(dup_overlap)[:10])

number of duplicate rows:  56
Removed genes that were also duplicates: 0
Example overlapping genes: []

dropped_cells = ss_mcf7_raw.shape[1] - ss_mcf7_filt.shape[1]
print(f"Number of removed cells: {dropped_cells}")

Number of removed cells: 70

qc_cells = pd.DataFrame({
    "total_counts": ss_mcf7_raw.sum(axis=0),
    "n_genes": (ss_mcf7_raw > 0).sum(axis=0)
})

retained_cells = ss_mcf7_filt.columns
dropped_cells = ss_mcf7_raw.columns.difference(retained_cells)

qc_retained = qc_cells.loc[retained_cells]
qc_dropped = qc_cells.loc[dropped_cells]

print("Retained cells:")
print(qc_retained.describe())

print("\nDropped cells:")
print(qc_dropped.describe())

Retained cells:
       total_counts       n_genes
count  3.130000e+02    313.000000
mean   1.158035e+06  10046.894569
std    3.964047e+05   1258.586404
min    2.633690e+05   5358.000000
25%    8.801830e+05   9326.000000
50%    1.199119e+06  10242.000000
75%    1.460597e+06  10922.000000
max    1.982470e+06  12260.000000

Dropped cells:
       total_counts       n_genes
count  7.000000e+01     70.000000
mean   2.638760e+05   4998.542857
std    5.509890e+05   3444.854204
min    1.000000e+00      1.000000
25%    6.060000e+03   2196.250000
50%    7.184050e+04   5204.000000
75%    1.470945e+05   8015.750000
max    2.308057e+06  12519.000000

# Apply a candidate filter
# We only keep the genes that have more than 250,000 total counts and more than 5000 genes
cell_mask = (qc_cells['total_counts'] > 250_000) & (qc_cells['n_genes'] > 5000)
filtered_candidate = set(qc_cells.index[cell_mask])

# Cells in the original filtered dataset
original_filtered = set(ss_mcf7_filt.columns)

# Overlap
overlap = filtered_candidate.intersection(original_filtered)
print(f"Candidate filter retains: {len(filtered_candidate)} cells")
print(f"Overlap with ss_mcf7_filt: {len(overlap)} / {len(original_filtered)} ({len(overlap)/len(original_filtered)*100:.1f}%)")

Candidate filter retains: 320 cells
Overlap with ss_mcf7_filt: 313 / 313 (100.0%)

# Genes in filtered but not in normalised
dropped_genes = ss_mcf7_filt.index.difference(ss_mcf7_norm.index)
print(f"Genes dropped during normalization: {len(dropped_genes)}")

Genes dropped during normalization: 15945

dropped_cells = ss_mcf7_filt.columns.difference(ss_mcf7_norm.columns)
print(f"Cells dropped during normalization: {len(dropped_cells)}")

Cells dropped during normalization: 63

# Total counts per cell = sum of all gene expression values
total_counts_before = ss_mcf7_filt.sum(axis=0)

# Number of expressed genes (non-zero) per cell
n_genes_before = (ss_mcf7_filt > 0).sum(axis=0)

total_counts_after = ss_mcf7_norm.sum(axis=0)
n_genes_after = (ss_mcf7_norm > 0).sum(axis=0)

print(f"Average total counts (before): {total_counts_before.mean():.2f}")
print(f"Average total counts (after):  {total_counts_after.mean():.2f}\n")

print(f"Average n_genes per cell (before): {n_genes_before.mean():.2f}")
print(f"Average n_genes per cell (after):  {n_genes_after.mean():.2f}")

Average total counts (before): 1157815.77
Average total counts (after):  347700.15

Average n_genes per cell (before): 10011.00
Average n_genes per cell (after):  1091.34

# Combine total counts
ss_mcf7_total_counts = pd.DataFrame({
    'total_counts': pd.concat([total_counts_before, total_counts_after]),
    'stage': ['Before'] * len(total_counts_before) + ['After'] * len(total_counts_after)
})

# Combine gene counts
ss_mcf7_n_genes = pd.DataFrame({
    'n_genes': pd.concat([n_genes_before, n_genes_after]),
    'stage': ['Before'] * len(n_genes_before) + ['After'] * len(n_genes_after)
})


plt.figure(figsize=(12, 5))

# Violin plot for total counts
plt.subplot(1, 2, 1)
sns.violinplot(data=ss_mcf7_total_counts, x='stage', y='total_counts', hue='stage', palette='Set2', legend=False)
plt.title("Total Counts per Cell")
plt.xlabel("")

# Violin plot for # of genes
plt.subplot(1, 2, 2)
sns.violinplot(data=ss_mcf7_n_genes, x='stage', y='n_genes', hue='stage', palette='Set2', legend=False)
plt.title("Number of Genes per Cell")
plt.xlabel("")

plt.suptitle("Before vs After Normalization", fontsize=14)
plt.tight_layout()
plt.show()

# How much does mean (log-transformed) expression vary across cells
print("Filtered + log2 mean variance:", np.log2(ss_mcf7_filt + 1).var(axis=1).mean())
print("Normalised + log2 mean variance:", np.log2(ss_mcf7_norm + 1).var(axis=1).mean())

Filtered + log2 mean variance: 2.7114283938366706
Normalised + log2 mean variance: 3.9135395503089314

ss_mcf7_filt_log = np.log2(ss_mcf7_filt + 1)
ss_mcf7_norm_log = np.log2(ss_mcf7_norm + 1)

filt_gene_var_log = ss_mcf7_filt_log.var(axis=1)
norm_gene_var_log = ss_mcf7_norm_log.var(axis=1)

fig, axes = plt.subplots(1, 2, figsize=(12, 5))

# Raw variance
sns.histplot(filt_gene_var_log, bins=100, color='red', stat='density', ax=axes[0])
axes[0].set_title("Log2 Gene Variance in Filtered Data")
axes[0].set_xlabel("Variance")

# Log-transformed variance
sns.histplot(norm_gene_var_log, bins=100, color='blue', stat='density', ax=axes[1])
axes[1].set_title("Log2 Gene Variance in Normalised Data")
axes[1].set_xlabel("Variance")

plt.tight_layout()
plt.show()

# Calculate variance per gene in filtered data
gene_var = ss_mcf7_filt.var(axis=1)

# Get top 3000 highly variable genes
top_var_genes = gene_var.sort_values(ascending=False).head(3000)

# Compare to normalized gene set
kept_genes = ss_mcf7_norm.index

# How many retained genes overlap with the top variable genes?
overlap = kept_genes.intersection(top_var_genes.index)
print(f"{len(overlap)} of {len(kept_genes)} genes in normalized data are among the top 3000 variable genes.")

894 of 3000 genes in normalized data are among the top 3000 variable genes.

sc.pp.normalize_total(adata, target_sum=1e6)  # each cell ends up with a total of 1,000,000 counts

ss_mcf7_norm_like = ss_mcf7_filt.div(ss_mcf7_filt.sum(axis=0), axis=1) * 1e6  # manual reimplementation of Scanpy’s normalize_total
total_counts_norm_like = ss_mcf7_norm_like.sum(axis=0)

print("Total counts after rescaling to 1e6:", total_counts_norm_like.describe())  # we expect min = max = mean = 1e6

# Take the intersection of shared genes and cells (250, 3000)
common_cells = ss_mcf7_norm.columns.intersection(ss_mcf7_norm_like.columns)
common_genes = ss_mcf7_norm.index.intersection(ss_mcf7_norm_like.index)

diff = (ss_mcf7_norm.loc[common_genes, common_cells] - 
        ss_mcf7_norm_like.loc[common_genes, common_cells]).abs().mean().mean()  # mean across all genes and all cells

print(f"Mean abs difference to scaled-to-1e6 normalization: {diff:.2f}")

Total counts after rescaling to 1e6: count    3.130000e+02
mean     1.000000e+06
std      5.434847e-11
min      1.000000e+06
25%      1.000000e+06
50%      1.000000e+06
75%      1.000000e+06
max      1.000000e+06
dtype: float64
Mean abs difference to scaled-to-1e6 normalization: 19.31

# Flatten both matrices and extract the common genes/cells
flat_original = ss_mcf7_norm.loc[common_genes, common_cells].values.flatten()
flat_recreated = ss_mcf7_norm_like.loc[common_genes, common_cells].values.flatten()

# Compute Pearson correlation
cor = np.corrcoef(flat_original, flat_recreated)[0,1]
print(f"Pearson correlation of expression values: {cor:.4f}")

Pearson correlation of expression values: 0.9999

# Scanpy Normalisation

# Transpose the matrix to match AnnData convention: cells as rows
X = ss_mcf7_filt.T  # shape: (cells × genes)

# Convert to AnnData
adata = ad.AnnData(X=X)

# Optional: name the genes and cells
adata.var_names = ss_mcf7_filt.index
adata.obs_names = ss_mcf7_filt.columns

# Normalise total counts per cell (default target_sum is median library size)
sc.pp.normalize_total(adata)

# Convert back to DataFrame
adata_df = pd.DataFrame(adata.X, index=adata.obs_names, columns=adata.var_names)

# Transpose back so that both datasets are genes × cells
adata_df_T = adata_df.T

# Get common genes and cells
common_genes = ss_mcf7_norm.index.intersection(adata_df_T.index)
common_cells = ss_mcf7_norm.columns.intersection(adata_df_T.columns)

# Compute absolute difference
diff_matrix = (ss_mcf7_norm.loc[common_genes, common_cells] - 
               adata_df_T.loc[common_genes, common_cells]).abs()

# Mean absolute difference
mean_abs_diff = diff_matrix.mean().mean()
print(f"Mean absolute difference: {mean_abs_diff:.2f}")

# Pearson correlation between flattened matrices
flat_orig = ss_mcf7_norm.loc[common_genes, common_cells].values.flatten()
flat_scanpy = adata_df_T.loc[common_genes, common_cells].values.flatten()

cor = np.corrcoef(flat_orig, flat_scanpy)[0, 1]
print(f"Pearson correlation: {cor:.4f}")

Mean absolute difference: 0.77
Pearson correlation: 0.9999

# Quality Control for the dropped cells in the original space
dropped_cells = ss_mcf7_filt.columns.difference(ss_mcf7_norm.columns)
qc_metrics = pd.DataFrame({
    "total_counts": ss_mcf7_filt.sum(axis=0),
    "n_genes": (ss_mcf7_filt > 0).sum(axis=0)
})

qc_metrics.loc[dropped_cells].describe()

# Quality Control for the retained cells in the original space
cells_retained = ss_mcf7_norm.columns
qc_metrics.loc[cells_retained].describe()

# Subset filtered matrix to just the 3000 genes in ss_mcf7_norm
genes_to_keep = ss_mcf7_norm.index
cells_to_check = ss_mcf7_filt.columns.difference(ss_mcf7_norm.columns)

subset = ss_mcf7_filt.loc[genes_to_keep, cells_to_check]

# Number of expressed genes (non-zero) per dropped cell
nonzeros_per_cell = (subset > 0).sum(axis=0)
nonzeros_per_cell.describe()

count      63.000000
mean     1037.968254
std       149.883715
min       662.000000
25%       927.500000
50%      1070.000000
75%      1155.500000
max      1322.000000
dtype: float64

# Retained cells
cells_retained = ss_mcf7_norm.columns
subset_retained = ss_mcf7_filt.loc[genes_to_keep, cells_retained]

nonzeros_retained = (subset_retained > 0).sum(axis=0)
nonzeros_retained.describe()

count     250.00000
mean     1082.24800
std       167.51017
min       574.00000
25%       982.25000
50%      1092.00000
75%      1197.75000
max      1455.00000
dtype: float64

# Function to extract just the condition (Hypo or Norm)
def extract_condition(colname):
    return colname.split('_')[2]  # "Hypo" or "Norm"

# Apply to all cells in filtered data
condition_all = ss_mcf7_filt.columns.to_series().apply(extract_condition)

# Apply to only the 250 retained cells
condition_retained = condition_all.loc[ss_mcf7_norm.columns]

# Count full and retained condition distributions
print("Condition distribution in full filtered set (313 cells):")
print(condition_all.value_counts())

print("\nCondition distribution in retained normalized set (250 cells):")
print(condition_retained.value_counts())

Condition distribution in full filtered set (313 cells):
Norm    158
Hypo    155
Name: count, dtype: int64

Condition distribution in retained normalized set (250 cells):
Norm    126
Hypo    124
Name: count, dtype: int64

genes_norm = ss_mcf7_norm.index

ss_mcf7_norm_like = ss_mcf7_filt.div(ss_mcf7_filt.sum(axis=0), axis=1) * 1e5

gene_stats = pd.DataFrame({
    'mean': ss_mcf7_norm_like.mean(axis=1),
    'variance': ss_mcf7_norm_like.var(axis=1)
})

gene_stats_sorted = gene_stats.sort_values(by='variance', ascending=False)

top_3000_var = gene_stats_sorted.head(3000)
genes_predicted = top_3000_var.index

# How many genes overlap?
overlap = genes_predicted.intersection(genes_norm)
print(f"Overlap with ss_mcf7_norm genes (variance): {len(overlap)} / 3000")

gene_stats['dispersion'] = gene_stats['variance'] / gene_stats['mean']
gene_stats_filtered = gene_stats[gene_stats['mean'] > 0]  # avoid div by zero
top_3000_disp = gene_stats_filtered.sort_values(by='dispersion', ascending=False).head(3000)

overlap_disp = top_3000_disp.index.intersection(genes_norm)
print(f"Overlap with ss_mcf7_norm genes (dispersion): {len(overlap_disp)} / 3000")

Overlap with ss_mcf7_norm genes (variance): 930 / 3000
Overlap with ss_mcf7_norm genes (dispersion): 1572 / 3000

# Use our normalisation
ss_mcf7_norm_full = adata_df.T

# Convert to AnnData
adata = anndata.AnnData(X=np.log1p(ss_mcf7_norm_full.T.astype(float)))  # See Note below
adata.var_names = ss_mcf7_filt.index
adata.obs_names = ss_mcf7_filt.columns

# Run HVG selection on the full gene set
sc.pp.highly_variable_genes(
    adata,
    flavor='seurat',
    n_top_genes=3000,
    inplace=True
)

# Filter to top 3,000 genes
adata = adata[:, adata.var['highly_variable']]

# Get the 3000 HVG gene names just selected by Scanpy
hvgs_from_filt = adata.var_names

# Get the original 3000 gene names from the ss_mcf7_norm matrix
hvgs_from_norm = ss_mcf7_norm.index

# Compute overlap
overlap = hvgs_from_filt.intersection(hvgs_from_norm)
print(f"Overlap: {len(overlap)} / 3000")
print(f"Percenatge Overlap: {len(overlap)/ 3000 * 100}")

Overlap: 2346 / 3000
Percenatge Overlap: 78.2

ss_hcc_raw_filt.shape

(23339, 233)

ss_hcc_filt.shape

(19503, 227)

genes_raw = set(ss_hcc_raw.index)
genes_filtered = set(ss_hcc_filt.index)

dropped_genes = genes_raw - genes_filtered
print(f"Genes dropped: {len(dropped_genes)}")

Genes dropped: 3893

# Genes that are expressed in more than 5 cells
ss_hcc_raw_genes_mask = (ss_hcc_raw > 0).sum(axis=1) > 5  # higher threshold results in less than 19503 genes remaining
ss_hcc_raw_gene_set = set(ss_hcc_raw.index[ss_hcc_raw_genes_mask])

print(f"Genes passing our threshold: {len(ss_hcc_raw_gene_set)}")

Genes passing our threshold: 19681

ss_hcc_raw_gene_set = ss_hcc_raw.index[ss_hcc_raw_genes_mask]

ss_hcc_filt_gene_set = ss_hcc_filt.index

overlap = ss_hcc_raw_gene_set.intersection(ss_hcc_filt_gene_set)
print(f"Overlap: {len(overlap)} / {len(ss_hcc_filt_gene_set)}")

Overlap: 19503 / 19503

# Convert Indexes to sets
ss_hcc_raw_gene_set = set(ss_hcc_raw_gene_set)
ss_hcc_filt_gene_set = set(ss_hcc_filt_gene_set)

# Find extra genes (present in the threshold but not in the filtered set)
extra_genes = ss_hcc_raw_gene_set - ss_hcc_filt_gene_set
extra_genes_list = list(extra_genes)
ss_hcc_raw.loc[extra_genes_list].mean(axis=1).describe()

count    178.000000
mean       0.321589
std        0.438802
min        0.024691
25%        0.058642
50%        0.164609
75%        0.421811
max        3.720165
dtype: float64

dropped_cells = ss_hcc_raw.shape[1] - ss_hcc_filt.shape[1]
print(f"Number of removed cells: {dropped_cells}")

Number of removed cells: 16

qc_cells = pd.DataFrame({
    "total_counts": ss_hcc_raw.sum(axis=0),
    "n_genes": (ss_hcc_raw > 0).sum(axis=0)
})

retained_cells = ss_hcc_filt.columns
dropped_cells = ss_hcc_raw.columns.difference(retained_cells)

qc_retained = qc_cells.loc[retained_cells]
qc_dropped = qc_cells.loc[dropped_cells]

print("Retained cells:")
print(qc_retained.describe())

print("\nDropped cells:")
print(qc_dropped.describe())

Retained cells:
       total_counts       n_genes
count  2.270000e+02    227.000000
mean   2.095821e+06  10735.555066
std    1.084443e+06   1025.490256
min    3.421620e+05   7361.000000
25%    1.028269e+06  10260.500000
50%    2.157315e+06  10881.000000
75%    2.965222e+06  11431.500000
max    4.858841e+06  12698.000000

Dropped cells:
       total_counts       n_genes
count  1.600000e+01     16.000000
mean   8.274348e+05   4581.625000
std    1.683270e+06   5370.398208
min    1.140000e+02     35.000000
25%    4.207500e+02     84.000000
50%    3.734050e+04    884.000000
75%    2.899445e+05   9234.500000
max    5.758132e+06  13986.000000

# Apply a candidate filter
cell_mask = (qc_cells['total_counts'] > 250_000) & (qc_cells['n_genes'] > 4000)
filtered_candidate = set(qc_cells.index[cell_mask])

# Cells in the original filtered dataset
original_filtered = set(ss_hcc_filt.columns)

# Overlap
overlap = filtered_candidate.intersection(original_filtered)
print(f"Candidate filter retains: {len(filtered_candidate)} cells")
print(f"Overlap with ss_hcc_filt: {len(overlap)} / {len(original_filtered)} ({len(overlap)/len(original_filtered)*100:.1f}%)")

Candidate filter retains: 230 cells
Overlap with ss_hcc_filt: 227 / 227 (100.0%)

# Genes in filtered but not in normalised
dropped_genes = ss_hcc_filt.index.difference(ss_hcc_norm.index)
print(f"Genes dropped during normalization: {len(dropped_genes)}")

Genes dropped during normalization: 16503

dropped_cells = ss_hcc_filt.columns.difference(ss_hcc_norm.columns)
print(f"Cells dropped during normalization: {len(dropped_cells)}")

Cells dropped during normalization: 45

# Total counts per cell = sum of all gene expression values
total_counts_before = ss_hcc_filt.sum(axis=0)

# Number of expressed genes (non-zero) per cell
n_genes_before = (ss_hcc_filt > 0).sum(axis=0)

total_counts_after = ss_hcc_norm.sum(axis=0)
n_genes_after = (ss_hcc_norm > 0).sum(axis=0)

print(f"Average total counts (before): {total_counts_before.mean():.2f}")
print(f"Average total counts (after):  {total_counts_after.mean():.2f}\n")

print(f"Average n_genes per cell (before): {n_genes_before.mean():.2f}")
print(f"Average n_genes per cell (after):  {n_genes_after.mean():.2f}")

Average total counts (before): 2095393.92
Average total counts (after):  502580.62

Average n_genes per cell (before): 10686.55
Average n_genes per cell (after):  880.40

# Combine total counts
ss_hcc_total_counts = pd.DataFrame({
    'total_counts': pd.concat([total_counts_before, total_counts_after]),
    'stage': ['Before'] * len(total_counts_before) + ['After'] * len(total_counts_after)
})

# Combine gene counts
ss_hcc_n_genes = pd.DataFrame({
    'n_genes': pd.concat([n_genes_before, n_genes_after]),
    'stage': ['Before'] * len(n_genes_before) + ['After'] * len(n_genes_after)
})


plt.figure(figsize=(12, 5))

# Violin plot for total counts
plt.subplot(1, 2, 1)
sns.violinplot(data=ss_hcc_total_counts, x='stage', y='total_counts', hue='stage', palette='Set2', legend=False)
plt.title("Total Counts per Cell")
plt.xlabel("")

# Violin plot for # of genes
plt.subplot(1, 2, 2)
sns.violinplot(data=ss_hcc_n_genes, x='stage', y='n_genes', hue='stage', palette='Set2', legend=False)
plt.title("Number of Genes per Cell")
plt.xlabel("")

plt.suptitle("Before vs After Normalization", fontsize=14)
plt.tight_layout()
plt.show()

print("Filtered + log2 mean variance:", np.log2(ss_hcc_filt + 1).var(axis=1).mean())
print("Normalised + log2 mean variance:", np.log2(ss_hcc_norm + 1).var(axis=1).mean())

Filtered + log2 mean variance: 3.11473854285522
Normalised + log2 mean variance: 3.076855767159178

# Scanpy Normalisation

# Transpose the matrix to match AnnData convention: cells as rows
X = ss_hcc_filt.T  # shape: (cells × genes)

# Convert to AnnData
adata = ad.AnnData(X=X)

# Optional: name the genes and cells
adata.var_names = ss_hcc_filt.index
adata.obs_names = ss_hcc_filt.columns

# Normalise total counts per cell (default target_sum is median library size)
sc.pp.normalize_total(adata)

# Our normalized + log-transformed matrix is now in:
adata.X  # (sparse or dense depending on input)

# Convert back to DataFrame
adata_df = pd.DataFrame(adata.X, index=adata.obs_names, columns=adata.var_names)

# Transpose back, so that both datasets are genes × cells
adata_df_T = adata_df.T

# Get common genes and cells
common_genes = ss_hcc_norm.index.intersection(adata_df_T.index)
common_cells = ss_hcc_norm.columns.intersection(adata_df_T.columns)

# Compute absolute difference
diff_matrix = (ss_hcc_norm.loc[common_genes, common_cells] - 
               adata_df_T.loc[common_genes, common_cells]).abs()

# 1. Mean absolute difference
mean_abs_diff = diff_matrix.mean().mean()
print(f"Mean absolute difference: {mean_abs_diff:.2f}")

# 2. Pearson correlation between flattened matrices
flat_orig = ss_hcc_norm.loc[common_genes, common_cells].values.flatten()
flat_scanpy = adata_df_T.loc[common_genes, common_cells].values.flatten()

cor = np.corrcoef(flat_orig, flat_scanpy)[0, 1]
print(f"Pearson correlation: {cor:.4f}")

Mean absolute difference: 1.38
Pearson correlation: 0.9998

# Quality Control for the dropped cells in the original space
dropped_cells = ss_hcc_filt.columns.difference(ss_hcc_norm.columns)
qc_metrics = pd.DataFrame({
    "total_counts": ss_hcc_filt.sum(axis=0),
    "n_genes": (ss_hcc_filt > 0).sum(axis=0)
})

qc_metrics.loc[dropped_cells].describe()

# Quality Control for the retained cells in the original space
cells_retained = ss_hcc_norm.columns
qc_metrics.loc[cells_retained].describe()

# Subset filtered matrix to just the 3000 genes in ss_mcf7_norm
genes_to_keep = ss_hcc_norm.index
cells_to_check = ss_hcc_filt.columns.difference(ss_hcc_norm.columns)

subset = ss_hcc_filt.loc[genes_to_keep, cells_to_check]

# Number of expressed genes (non-zero) per dropped cell
nonzeros_per_cell = (subset > 0).sum(axis=0)
nonzeros_per_cell.describe()

count      45.000000
mean      792.533333
std       116.710715
min       549.000000
25%       720.000000
50%       778.000000
75%       847.000000
max      1104.000000
dtype: float64

# Retained cells
subset_retained = ss_hcc_filt.loc[genes_to_keep, cells_retained]
# Number of expressed genes (non-zero) per retained cell
nonzeros_retained = (subset_retained > 0).sum(axis=0)
nonzeros_retained.describe()

count     182.000000
mean      870.175824
std       115.900805
min       549.000000
25%       793.500000
50%       872.500000
75%       944.500000
max      1169.000000
dtype: float64

# Step 1: Use our normalisation
ss_hcc_norm_full = adata_df.T

# Step 2: Convert to AnnData
adata = anndata.AnnData(X=np.log1p(ss_hcc_norm_full.T.astype(float)))
adata.var_names = ss_hcc_filt.index
adata.obs_names = ss_hcc_filt.columns

# Step 3: Run HVG selection on the full gene set
sc.pp.highly_variable_genes(
    adata,
    flavor='seurat',
    n_top_genes=3000,
    inplace=True
)

# Step 4: Filter to top 3,000 genes
adata = adata[:, adata.var['highly_variable']]

# Get the 3000 HVG gene names just selected by Scanpy
hvgs_from_filt = adata.var_names

# Get the original 3000 gene names from the ss_mcf7_norm matrix
hvgs_from_norm = ss_hcc_norm.index

# Compute overlap
overlap = hvgs_from_filt.intersection(hvgs_from_norm)
print(f"Overlap: {len(overlap)} / 3000")
print(f"Percenatge Overlap: {len(overlap)/ 3000 * 100}")

Overlap: 2080 / 3000
Percenatge Overlap: 69.33333333333334

ds_mcf7_norm = pd.read_csv("AILab2025/DropSeq/MCF7_Filtered_Normalised_3000_Data_train.txt",delimiter=" ",engine='python',index_col=0)
ds_hcc_norm = pd.read_csv("AILab2025/DropSeq/HCC1806_Filtered_Normalised_3000_Data_train.txt",delimiter=" ",engine='python',index_col=0)

ds_mcf7_norm.shape

(3000, 21626)

ds_mcf7_norm.head(5)

ds_mcf7_norm.describe()

ds_hcc_norm.shape

(3000, 14682)

ds_hcc_norm.head(5)

ds_hcc_norm.describe()

smart_zero_prop = (ss_mcf7_norm == 0).sum().sum() / ss_mcf7_norm.size
drop_zero_prop = (ds_mcf7_norm == 0).sum().sum() / ds_mcf7_norm.size

print(f"Sparsity (Smart-seq MCF7): {smart_zero_prop:.2%}")
print(f"Sparsity (Drop-seq MCF7):  {drop_zero_prop:.2%}")

Sparsity (Smart-seq MCF7): 63.62%
Sparsity (Drop-seq MCF7):  97.53%

smart_zero_prop = (ss_hcc_norm == 0).sum().sum() / ss_hcc_norm.size
drop_zero_prop = (ds_hcc_norm == 0).sum().sum() / ds_hcc_norm.size

print(f"Sparsity (Smart-seq HCC1806): {smart_zero_prop:.2%}")
print(f"Sparsity (Drop-seq HCC1806):  {drop_zero_prop:.2%}")

Sparsity (Smart-seq HCC1806): 70.65%
Sparsity (Drop-seq HCC1806):  97.64%

hypo_count = sum('hypo' in col.lower() for col in ds_mcf7_norm.columns)
norm_count = sum('norm' in col.lower() for col in ds_mcf7_norm.columns)

print(f"Hypoxic samples: {hypo_count}")
print(f"Normoxic samples: {norm_count}")

total = hypo_count + norm_count
print(f"Hypoxic: {hypo_count/total:.2%}, Normoxic: {norm_count/total:.2%}")

Hypoxic samples: 8921
Normoxic samples: 12705
Hypoxic: 41.25%, Normoxic: 58.75%

hypo_count = sum('hypo' in col.lower() for col in ds_hcc_norm.columns)
norm_count = sum('norm' in col.lower() for col in ds_hcc_norm.columns)

print(f"Hypoxic samples: {hypo_count}")
print(f"Normoxic samples: {norm_count}")

total = hypo_count + norm_count
print(f"Hypoxic: {hypo_count/total:.2%}, Normoxic: {norm_count/total:.2%}")

Hypoxic samples: 8899
Normoxic samples: 5783
Hypoxic: 60.61%, Normoxic: 39.39%

def mean_pairwise_correlation(df, method='pearson'):
    """
    Computes the mean pairwise correlation between columns.

    Parameters:
        df (pd.DataFrame): The expression matrix.
        method (str): 'pearson', 'spearman', or 'kendall'.

    Returns:
        float: Mean pairwise correlation (excluding self-correlations).
    """
    cor_matrix = df.corr(method=method)
    upper_tri_values = cor_matrix.values[np.triu_indices_from(cor_matrix, k=1)]
    return upper_tri_values.mean()

print("Mean pairwise cell–cell correlation")
print(f"Raw MCF7: {mean_pairwise_correlation(ss_mcf7_raw):.4f}")
print(f"Filtered MCF7: {mean_pairwise_correlation(ss_mcf7_filt):.4f}")
print(f"Normalised MCF7: {mean_pairwise_correlation(ss_mcf7_norm):.4f}")
print(f"Raw HCC1806: {mean_pairwise_correlation(ss_hcc_raw):.4f}")
print(f"Filetered HCC1806: {mean_pairwise_correlation(ss_hcc_filt):.4f}")
print(f"Normalised HCC1806: {mean_pairwise_correlation(ss_hcc_norm):.4f}")

Mean pairwise cell–cell correlation
Raw MCF7: 0.6720
Filtered MCF7: 0.7211
Normalised MCF7: 0.6654
Raw HCC1806: 0.7405
Filetered HCC1806: 0.7985
Normalised HCC1806: 0.7480

warnings.filterwarnings('ignore')

def get_top_components(adata, n_pcs, plot=True):
    """this function runs PCA and returns the number of components needed to explain 95% of the variance
    Parameters: 
    adata: AnnData object
    n_pcs: number of principal components to compute
    plot: whether to plot the variance explained per PC (scree plot)
    Returns:
    n_components_95: number of components needed to explain 95% of the variance
    Additionally it adds the following to the adata object:
    adata.uns['pca']['n_components_95']: number of components needed to explain 95% of the variance
    adata.obsm['X_pca_95']: PCA coordinates for the first n_components_95
    """
    # plot variance explained per PC (scree plot)
    if plot:
        sc.pl.pca_variance_ratio(adata, n_pcs=n_pcs, log=False)

    # access the variance ratios
    explained_var = adata.uns['pca']['variance_ratio']  # array of variance explained per PC

    # cmpute cumulative sum
    cumulative_var = np.cumsum(explained_var)
    
    # find the number of PCs needed to reach 95% variance
    n_components_95 = np.argmax(cumulative_var >= 0.95) + 1  # +1 because np.argmax is 0-based
    print(f"Number of PCs needed to explain 95% variance: {n_components_95}")

    # add to the adata object this information
    adata.uns['pca']['n_components_95'] = n_components_95
    adata.obsm['X_pca_95'] = adata.obsm['X_pca'][:, :n_components_95]

    for i, var in enumerate(explained_var[:10]):
        print(f"PC{i+1} explains: {var*100:.2f}%")
    return n_components_95

def add_labels(adata):
    """this function will add labels to the adata object based on the cell names
    """
    # add labels based on cell names
    adata.obs['condition'] = ['Hypo' if 'hypo' in name.lower() else 'Norm' for name in adata.obs_names]

def best_linear_pc_split(adata, label_key='condition', max_pc=20):
    """
    Finds the best pair of principal components (among first `max_pc`) for linearly separating the data.

    Parameters:
    - adata: AnnData object with PCA computed (`adata.obsm['X_pca']` must exist).
    - label_key: column in `adata.obs` used as target for classification.
    - max_pc: maximum number of PCs to consider (default=20).

    Returns:
    - best_pair: tuple (pc1, pc2) with 1-based PC indices.
    - best_score: mean cross-validation accuracy.
    """

    if 'X_pca' not in adata.obsm:
        raise ValueError("Run sc.tl.pca(adata) first to compute PCA")

    X_pca = adata.obsm['X_pca'][:, :max_pc]
    y = adata.obs[label_key].values

    best_pair = None
    best_score = -np.inf

    for i, j in combinations(range(max_pc), 2):
        X_pair = X_pca[:, [i, j]]
        clf = LogisticRegression(max_iter=1000)
        score = cross_val_score(clf, X_pair, y, cv=5).mean()

        if score > best_score:
            best_score = score
            best_pair = (i + 1, j + 1)  # return 1-based PC indices

    return best_pair, best_score

def best_split(adata, label_key='condition', max_pc=20):
    """draws the best split plot"""
    best_pair, best_score = best_linear_pc_split(adata, label_key=label_key, max_pc=max_pc)
    print(f"Best pair of PCs: {best_pair} with score: {best_score:.4f}")
    plt.figure(figsize=(8, 6))
    sns.scatterplot(x=adata.obsm['X_pca'][:, best_pair[0] - 1],
                    y=adata.obsm['X_pca'][:, best_pair[1] - 1],
                    hue=adata.obs[label_key],
                    palette='Set2',
                    s = 10)
    plt.title(f"Best pair of PCs: {best_pair} with score: {best_score:.4f}")
    plt.xlabel(f"PC{best_pair[0]}")
    plt.ylabel(f"PC{best_pair[1]}")
    plt.legend(title='condition')
    plt.show()

X = ss_mcf7_norm.T               # cells × genes
adata_ss_mcf7 = ad.AnnData(X) 
adata_ss_mcf7.obs_names = ss_mcf7_norm.columns      # adding cell names
adata_ss_mcf7.var_names = ss_mcf7_norm.index        # adding gene names

add_labels(adata_ss_mcf7)           # adding condition labels
sc.pp.pca(adata_ss_mcf7, n_comps=50, svd_solver='arpack', use_highly_variable=False)
get_top_components(adata_ss_mcf7, n_pcs=50, plot=True) 

# plot PCA
best_split(adata_ss_mcf7, label_key='condition', max_pc=10)

Number of PCs needed to explain 95% variance: 20
PC1 explains: 63.45%
PC2 explains: 9.11%
PC3 explains: 6.27%
PC4 explains: 4.03%
PC5 explains: 3.16%
PC6 explains: 1.54%
PC7 explains: 1.14%
PC8 explains: 1.01%
PC9 explains: 0.90%
PC10 explains: 0.76%
Best pair of PCs: (1, 6) with score: 0.9920

X_log = np.log1p(ss_mcf7_norm.T)        # cells × genes
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X_log)       # scaling the data
adata_ss_mcf7_scaled = ad.AnnData(X_log)
adata_ss_mcf7_scaled.obs_names = ss_mcf7_norm.columns       # adding cell names
adata_ss_mcf7_scaled.var_names = ss_mcf7_norm.index         # adding gene names

add_labels(adata_ss_mcf7_scaled) # adding condition labels
sc.pp.pca(adata_ss_mcf7_scaled, n_comps=249, svd_solver='arpack', use_highly_variable=False)
get_top_components(adata_ss_mcf7_scaled, n_pcs=249, plot=True)

# plot PCA 
best_split(adata_ss_mcf7_scaled, label_key='condition', max_pc=50)

Number of PCs needed to explain 95% variance: 204
PC1 explains: 18.64%
PC2 explains: 5.55%
PC3 explains: 4.02%
PC4 explains: 2.24%
PC5 explains: 1.77%
PC6 explains: 1.47%
PC7 explains: 1.26%
PC8 explains: 1.16%
PC9 explains: 1.04%
PC10 explains: 0.97%
Best pair of PCs: (1, 2) with score: 0.9920

data = ss_hcc_norm

X = data.T          # cells × genes
adata_ss_hcc = ad.AnnData(X) 
adata_ss_hcc.obs_names = data.columns          # adding cell names
adata_ss_hcc.var_names = data.index            # adding gene names

add_labels(adata_ss_hcc)        # adding condition labels
sc.pp.pca(adata_ss_hcc, n_comps=50, svd_solver='arpack', use_highly_variable=False)
get_top_components(adata_ss_hcc, n_pcs=50, plot=True)  

# plot PCA
best_split(adata_ss_hcc, label_key='condition', max_pc=10)

Number of PCs needed to explain 95% variance: 34
PC1 explains: 29.02%
PC2 explains: 18.10%
PC3 explains: 12.29%
PC4 explains: 7.97%
PC5 explains: 4.96%
PC6 explains: 3.64%
PC7 explains: 2.74%
PC8 explains: 2.11%
PC9 explains: 1.74%
PC10 explains: 1.32%
Best pair of PCs: (2, 3) with score: 0.9450

data = ss_hcc_norm
scaler = StandardScaler()
X_log = np.log1p(data.T)        # cells × genes
X_scaled = scaler.fit_transform(X_log)          # scaling the data
adata_ss_hcc_scaled = ad.AnnData(X_scaled)
adata_ss_hcc_scaled.obs_names = data.columns        # adding cell names
adata_ss_hcc_scaled.var_names = data.index       # adding gene names

add_labels(adata_ss_hcc_scaled)      # adding condition labels
sc.pp.pca(adata_ss_hcc_scaled, n_comps=181, svd_solver='arpack', use_highly_variable=False)
get_top_components(adata_ss_hcc_scaled, n_pcs=181)

# plot PCA 
best_split(adata_ss_hcc_scaled, label_key='condition', max_pc=10)

Number of PCs needed to explain 95% variance: 161
PC1 explains: 4.50%
PC2 explains: 3.23%
PC3 explains: 2.56%
PC4 explains: 1.97%
PC5 explains: 1.55%
PC6 explains: 1.46%
PC7 explains: 1.31%
PC8 explains: 1.21%
PC9 explains: 1.07%
PC10 explains: 0.99%
Best pair of PCs: (2, 3) with score: 0.9012

data = ds_mcf7_norm

X = data.T          # cells × genes
print(X.shape)
adata_ds_mcf7 = ad.AnnData(X) 
adata_ds_mcf7.obs_names = data.columns      # adding cell names
adata_ds_mcf7.var_names = data.index        # adding gene names

add_labels(adata_ds_mcf7)       # adding condition labels
sc.pp.pca(adata_ds_mcf7, n_comps=800, svd_solver='arpack', use_highly_variable=False)
get_top_components(adata_ds_mcf7, n_pcs=800, plot=True)  
# plot PCA
best_split(adata_ds_mcf7, label_key='condition', max_pc=10)

(21626, 3000)

Number of PCs needed to explain 95% variance: 761
PC1 explains: 24.95%
PC2 explains: 8.63%
PC3 explains: 4.43%
PC4 explains: 2.63%
PC5 explains: 2.13%
PC6 explains: 1.42%
PC7 explains: 1.30%
PC8 explains: 1.02%
PC9 explains: 0.92%
PC10 explains: 0.85%
Best pair of PCs: (2, 3) with score: 0.8690

data = ds_mcf7_norm
X_log = np.log1p(data.T)        # cells × genes
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X_log)          # scaling the data
adata_ds_mcf7_scaled = ad.AnnData(X_scaled)
adata_ds_mcf7_scaled.obs_names = data.columns       # adding cell names
adata_ds_mcf7_scaled.var_names = data.index         # adding gene names

add_labels(adata_ds_mcf7_scaled)        # adding condition labels
sc.pp.pca(adata_ds_mcf7_scaled, n_comps=2999, svd_solver='arpack', use_highly_variable=False)
get_top_components(adata_ds_mcf7_scaled, n_pcs=2999)

# plot PCA
best_split(adata_ds_mcf7_scaled, label_key='condition', max_pc=10)

Number of PCs needed to explain 95% variance: 2652
PC1 explains: 0.73%
PC2 explains: 0.29%
PC3 explains: 0.17%
PC4 explains: 0.12%
PC5 explains: 0.09%
PC6 explains: 0.09%
PC7 explains: 0.08%
PC8 explains: 0.08%
PC9 explains: 0.07%
PC10 explains: 0.07%
Best pair of PCs: (1, 3) with score: 0.9681

data = ds_hcc_norm

X = data.T         # cells × genes
adata_ds_hcc = ad.AnnData(X) 
adata_ds_hcc.obs_names = data.columns       # adding cell names
adata_ds_hcc.var_names = data.index         # adding gene names

add_labels(adata_ds_hcc)        # adding condition labels
sc.pp.pca(adata_ds_hcc, n_comps=900, svd_solver='arpack', use_highly_variable=False)
get_top_components(adata_ds_hcc, n_pcs=900, plot=True)  

# plot PCA
best_split(adata_ds_hcc, label_key='condition', max_pc=10)

Number of PCs needed to explain 95% variance: 844
PC1 explains: 8.42%
PC2 explains: 6.32%
PC3 explains: 3.88%
PC4 explains: 2.87%
PC5 explains: 2.38%
PC6 explains: 2.16%
PC7 explains: 1.67%
PC8 explains: 1.48%
PC9 explains: 1.44%
PC10 explains: 1.25%
Best pair of PCs: (5, 6) with score: 0.8087

data = ds_hcc_norm
X_log = np.log1p(data.T)    # cells × genes
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X_log)       # scaling the data
adata_ds_hcc_scaled = ad.AnnData(X_scaled) 
adata_ds_hcc_scaled.obs_names = data.columns           # adding cell names
adata_ds_hcc_scaled.var_names = data.index             # adding gene names

add_labels(adata_ds_hcc_scaled)         # adding condition labels
sc.pp.pca(adata_ds_hcc_scaled, n_comps=2999, svd_solver='arpack', use_highly_variable=False)
get_top_components(adata_ds_hcc_scaled, n_pcs=2999, plot=True)  

# plot PCA
best_split(adata_ds_hcc_scaled, label_key='condition', max_pc=10)

Number of PCs needed to explain 95% variance: 2595
PC1 explains: 0.39%
PC2 explains: 0.27%
PC3 explains: 0.17%
PC4 explains: 0.15%
PC5 explains: 0.09%
PC6 explains: 0.09%
PC7 explains: 0.08%
PC8 explains: 0.08%
PC9 explains: 0.08%
PC10 explains: 0.08%
Best pair of PCs: (3, 4) with score: 0.8932

sc.tl.pca(adata_ds_mcf7_scaled, n_comps=200, svd_solver='full')

evr    = adata_ds_mcf7_scaled.uns['pca']['variance_ratio']        # shape
cumvar = np.cumsum(evr)                           # cumulative sum
pcs    = np.arange(1, len(evr) + 1)               # [1,2,3,...]

fig, ax1 = plt.subplots(figsize=(6,4))
ax1.plot(pcs, evr,    '-o', label='per‐PC var.')
ax1.set_xlabel('PC number');   ax1.set_ylabel('Explained variance ratio')
ax1.axvline(20, color='gray', linestyle='--', alpha=0.5)

ax2 = ax1.twinx()
ax2.plot(pcs, cumvar, '-s', c='C1', label='cumulative var.')
ax2.set_ylabel('Cumulative variance')

ax1.legend(loc='upper left');  ax2.legend(loc='lower right')
plt.title('Scree & cumulative variance')
plt.show()

sc.tl.pca(adata_ds_hcc_scaled, n_comps=200, svd_solver='full')

evr    = adata_ds_hcc_scaled.uns['pca']['variance_ratio']        # shape
cumvar = np.cumsum(evr)                           # cumulative sum
pcs    = np.arange(1, len(evr) + 1)               # [1,2,3,...]

fig, ax1 = plt.subplots(figsize=(6,4))
ax1.plot(pcs, evr,    '-o', label='per‐PC var.')
ax1.set_xlabel('PC number');   ax1.set_ylabel('Explained variance ratio')
ax1.axvline(20, color='gray', linestyle='--', alpha=0.5)

ax2 = ax1.twinx()
ax2.plot(pcs, cumvar, '-s', c='C1', label='cumulative var.')
ax2.set_ylabel('Cumulative variance')

ax1.legend(loc='upper left');  ax2.legend(loc='lower right')
plt.title('Scree & cumulative variance')
plt.show()

data = ds_mcf7_norm
X_log = np.log1p(data.T)        # cells × genes
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X_log)          # scaling the data
adata_ds_mcf7_scaled = ad.AnnData(X_scaled)
adata_ds_mcf7_scaled.obs_names = data.columns       # adding cell names
adata_ds_mcf7_scaled.var_names = data.index         # adding gene names

add_labels(adata_ds_mcf7_scaled)        # adding condition labels
sc.pp.pca(adata_ds_mcf7_scaled, n_comps=10, svd_solver='arpack', use_highly_variable=False)

data = ds_hcc_norm
X_log = np.log1p(data.T)    # cells × genes
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X_log)       # scaling the data
adata_ds_hcc_scaled = ad.AnnData(X_scaled) 
adata_ds_hcc_scaled.obs_names = data.columns           # adding cell names
adata_ds_hcc_scaled.var_names = data.index             # adding gene names

add_labels(adata_ds_hcc_scaled)         # adding condition labels
sc.pp.pca(adata_ds_hcc_scaled, n_comps=10, svd_solver='arpack', use_highly_variable=False)

summary_df = pd.DataFrame([
    {"Dataset": "SmartSeq MCF7",     "Scaling": "Unscaled", "PCs for 95% var": 20,   "Best PC pair": "1,6", "Accuracy": 0.992},
    {"Dataset": "SmartSeq MCF7",     "Scaling": "Scaled",   "PCs for 95% var": 204,  "Best PC pair": "1,2", "Accuracy": 0.992},
    {"Dataset": "SmartSeq HCC1806",  "Scaling": "Unscaled", "PCs for 95% var": 34,   "Best PC pair": "2,3", "Accuracy": 0.945},
    {"Dataset": "SmartSeq HCC1806",  "Scaling": "Scaled",   "PCs for 95% var": 161,  "Best PC pair": "2,3", "Accuracy": 0.901},
    {"Dataset": "DropSeq MCF7",      "Scaling": "Unscaled", "PCs for 95% var": 761,  "Best PC pair": "2,3", "Accuracy": 0.869},
    {"Dataset": "DropSeq MCF7",      "Scaling": "Scaled",   "PCs for 95% var": 2652, "Best PC pair": "2,3", "Accuracy": 0.901},
    {"Dataset": "DropSeq HCC1806",   "Scaling": "Unscaled", "PCs for 95% var": 844,  "Best PC pair": "5,6", "Accuracy": 0.809},
    {"Dataset": "DropSeq HCC1806",   "Scaling": "Scaled",   "PCs for 95% var": 2595, "Best PC pair": "3,4", "Accuracy": 0.893},
])

summary_df

# printing the shape of the datasets to look at the number of cells and genes
print(ds_hcc_norm.shape,
ds_mcf7_norm.shape,
ss_hcc_norm.shape,
ss_mcf7_norm.shape)

(3000, 14682) (3000, 21626) (3000, 182) (3000, 250)

def build_and_diagnose_knn(
    adata,
    n_neighbors,
    metric="euclidean",
    use_rep="X_pca_95",
    random_state=42
):
    """
    Build a k-NN graph on adata.obsm['X_pca'] and then produce:
      1) degree‐distribution histogram
      2) adjacency heatmap for a random subset of cells
      3) spring‐layout network plot for that subset

    Parameters
    ----------
    adata : AnnData
        Must have PCA in adata.obsm['X_pca'].
    n_neighbors : int
        k for the k-NN graph.
    metric : str
        Distance metric for k-NN.
    random_state : int
        Seed for reproducibility.
    Returns
    -------
    None
    Additionally it adds the following to the adata object:
    adata.uns['neighbors']['degree_distribution']: degree distribution of the k-NN graph
    adata.obsm["connectivities"]: connectivity matrix of the k-NN graph
    """
    if use_rep is None:
        use_rep = "X_pca_95"  # default representation for k-NN graph
    # Check if elbow point is already computed

    # 1) build the graph
    sc.pp.neighbors(
        adata,
        n_neighbors=n_neighbors,
        metric='euclidean',  
        method='umap',
        knn=True,
        use_rep=f"{use_rep}",
        random_state=random_state
    )

build_and_diagnose_knn(
    adata_ss_mcf7,
    n_neighbors=int(np.sqrt(250)), # sqrt(250) = 15.81
    metric="euclidean",
    random_state=42
)

build_and_diagnose_knn(
    adata_ss_hcc,
    n_neighbors=int(np.sqrt(ss_hcc_norm.shape[1])), #sqrt(181) = 13.45
    metric="euclidean",
    random_state=42
)

build_and_diagnose_knn(
    adata_ds_mcf7_scaled,
    n_neighbors=int(np.sqrt(ds_mcf7_norm.shape[1])), # sqrt(21626) = 147.0
    metric="euclidean",
    use_rep="X_pca",
    random_state=42
)

build_and_diagnose_knn(
    adata_ds_hcc_scaled,
    n_neighbors=int(np.sqrt(ds_hcc_norm.shape[1])), # sqrt(14682) = 121.2
    use_rep="X_pca",
    metric="euclidean",
    random_state=42
)

def t_sne(adata, title="", perplexity=30, use_rep="X_pca_95", random_state=42):
    """
    Run t-SNE on the PCA-reduced data and plot the results.
    Parameters
    ----------
    adata : AnnData
        The AnnData object containing PCA-reduced data.
    title : str
        Title for the plot.
    perplexity : int
        Perplexity parameter for t-SNE.
    Returns
    -------
    adata : AnnData
        The AnnData object with t-SNE coordinates added.
    """
    sc.tl.tsne(
        adata,
        use_rep=use_rep,
        perplexity=perplexity,
        n_pcs=adata.uns["pca"]["n_components_95"] if use_rep == "X_pca_95" else None,
        random_state=42)

    sc.pl.tsne(
        adata,
        color='condition',
        show=False,
        size=10,
        title=f"t-SNE: {title}"
    )
    return adata

t_sne(adata_ss_mcf7, title="MCF7 Smart Seq unscaled", perplexity=50)

AnnData object with n_obs × n_vars = 250 × 3000
    obs: 'condition'
    uns: 'pca', 'neighbors', 'tsne', 'condition_colors'
    obsm: 'X_pca', 'X_pca_95', 'X_tsne'
    varm: 'PCs'
    obsp: 'distances', 'connectivities'

t_sne(adata_ss_hcc, title="HCC1806 Smart Seq unscaled", perplexity=20)

AnnData object with n_obs × n_vars = 182 × 3000
    obs: 'condition'
    uns: 'pca', 'neighbors', 'tsne', 'condition_colors'
    obsm: 'X_pca', 'X_pca_95', 'X_tsne'
    varm: 'PCs'
    obsp: 'distances', 'connectivities'

t_sne(adata_ds_mcf7_scaled, title="MCF7 Drop Seq scaled - using only 10 components", perplexity=50, use_rep="X_pca", random_state=42)

AnnData object with n_obs × n_vars = 21626 × 3000
    obs: 'condition'
    uns: 'pca', 'neighbors', 'tsne', 'condition_colors'
    obsm: 'X_pca', 'X_tsne'
    varm: 'PCs'
    obsp: 'distances', 'connectivities'

t_sne(adata_ds_hcc_scaled, title="HCC1806 Drop Seq scaled", perplexity=50, use_rep="X_pca")

AnnData object with n_obs × n_vars = 14682 × 3000
    obs: 'condition'
    uns: 'pca', 'neighbors', 'tsne', 'condition_colors'
    obsm: 'X_pca', 'X_tsne'
    varm: 'PCs'
    obsp: 'distances', 'connectivities'

sc.tl.umap(
    adata_ss_mcf7,
    min_dist=0.5,
    random_state=42
)
sc.pl.umap(
    adata_ss_mcf7,
    color='condition',
    show=False,
    size=20,
    title="UMAP: MCF7 Smart-seq unscaled"
)

<Axes: title={'center': 'UMAP: MCF7 Smart-seq unscaled'}, xlabel='UMAP1', ylabel='UMAP2'>

sc.tl.umap(
    adata_ss_hcc,
    min_dist=0.5,
    random_state=42
)
sc.pl.umap(
    adata_ss_hcc,
    color='condition',
    show=False,
    size=20,
    title="UMAP: HCC1806 Smart-seq unscaled"
)

<Axes: title={'center': 'UMAP: HCC1806 Smart-seq unscaled'}, xlabel='UMAP1', ylabel='UMAP2'>

#scaled version
sc.tl.umap(
    adata_ds_mcf7_scaled,
    min_dist=0.5,
    random_state=42
)
sc.pl.umap(
    adata_ds_mcf7_scaled,
    color='condition',
    show=False,
    size=10,
    title="UMAP: MCF7 Drop-seq scaled"
)

<Axes: title={'center': 'UMAP: MCF7 Drop-seq scaled'}, xlabel='UMAP1', ylabel='UMAP2'>

# scaled version
sc.tl.umap(
    adata_ds_hcc_scaled,
    min_dist=0.5,
    random_state=42
)
sc.pl.umap(
    adata_ds_hcc_scaled,
    color='condition',
    show=False,
    size=10,
    title="UMAP: HCC1806 Drop-seq scaled"
)

<Axes: title={'center': 'UMAP: HCC1806 Drop-seq scaled'}, xlabel='UMAP1', ylabel='UMAP2'>

def kmeans_optimization(
    adata,
    rep_key: str = 'X_pca_95',
    visual: str = 'pca',
    k_range: range = range(2, 10),
    random_state: int = 42
):
    """
    Optimize KMeans on a given embedding, store results in adata.uns and adata.obs.

    Parameters
    ----------
    adata: AnnData
        Must contain adata.obsm[rep_key] for clustering.
    rep_key: str
        Key in adata.obsm to cluster on (e.g. 'X_pca', 'X_pca_95', 'X_umap').
    visual: str
        Identifier under which results will be stored in adata.uns['kmeans'].
    k_range: range
        Range of k values to evaluate (n_clusters).
    random_state: int
        Random seed for reproducibility.

    Effects
    -------
    - Populates adata.uns['kmeans'][visual] = {
          'k_range': list(k_range),
          'inertia': [...],
          'silhouette': [...],
          'best_k': int,
          'best_score': float,
          'labels_key': str
      }
    - Stores best KMeans labels in adata.obs under key provided by 'labels_key'.
    """
    # prepare storage
    if 'kmeans' not in adata.uns:
        adata.uns['kmeans'] = {}
    results = {}

    # extract data for clustering
    X = adata.obsm.get(rep_key)
    if X is None:
        raise KeyError(f"adata.obsm['{rep_key}'] not found")

    inertias = []
    silhouettes = []
    models = []

    # fit and evaluate
    for k in k_range:
        model = KMeans(n_clusters=k, n_init=10, random_state=random_state).fit(X)
        inertias.append(model.inertia_)
        if k > 1:
            silhouettes.append(silhouette_score(X, model.labels_))
        else:
            silhouettes.append(np.nan)
        models.append(model)

    # determine best k by silhouette
    silhouettes_np = np.array(silhouettes)
    # ignore first nan
    best_idx = np.nanargmax(silhouettes_np)
    best_k = k_range[best_idx]
    best_score = silhouettes_np[best_idx]
    best_labels = models[best_idx].labels_.astype(str)
    labels_key = f'kmeans_{visual}'

    # store in adata
    results['k_range'] = list(k_range)
    results['inertia'] = inertias
    results['silhouette'] = silhouettes
    results['best_k'] = int(best_k)
    results['best_score'] = float(best_score)
    results['labels_key'] = labels_key

    adata.uns['kmeans'][visual] = results
    adata.obs[labels_key] = best_labels
    
    # plot inertia & silhouette
    fig, axs = plt.subplots(1, 2, figsize=(12, 4))
    axs[0].plot(list(k_range), inertias, 'bo-')
    axs[0].set_xlabel('k'), axs[0].set_ylabel('Inertia'), axs[0].set_title(f'Elbow Plot ({visual})')
    axs[1].plot(list(k_range), silhouettes, 'bo-')
    axs[1].set_xlabel('k'), axs[1].set_ylabel('Silhouette'), axs[1].set_title(f'Silhouette Scores ({visual})')
    plt.tight_layout()
    plt.show()

    return best_k

def silhouette_diagrams(
    adata,
    rep_key: str = 'X_pca_95',
    visual: str = 'pca',
    k_range: range = range(2, 7),
    dataset_name: str = 'Dataset'
):
    """
    Plot silhouette diagrams for KMeans clusters on a given embedding.

    Parameters
    ----------
    adata: AnnData
        Must contain adata.obsm[rep_key].
    rep_key: str
        Key in adata.obsm for clustering.
    visual: str
        Identifier used for titles and result storage.
    k_range: range
        Values of k to evaluate.
    dataset_name: str
        Name used in subplot titles.

    Effects
    -------
    - Stores silhouette_scores dict in adata.uns['kmeans'][visual]['silhouette_details']
    """
    X = adata.obsm.get(rep_key)
    if X is None:
        raise KeyError(f"adata.obsm['{rep_key}'] not found")

    k_list = list(k_range)
    models = [KMeans(n_clusters=k, n_init=10, random_state=42).fit(X) for k in k_list]
    sil_scores = [silhouette_score(X, m.labels_) for m in models]

    # Prepare plots
    n_plots = len(k_list)
    n_cols = 3
    n_rows = math.ceil(n_plots / n_cols)
    plt.figure(figsize=(5 * n_cols, 4 * n_rows))

    details = {}
    for idx, (k, model) in enumerate(zip(k_list, models)):
        ax = plt.subplot(n_rows, n_cols, idx + 1)
        labels = model.labels_
        coeffs = silhouette_samples(X, labels)
        details[k] = coeffs

        padding = len(X) // 30
        pos = padding
        ticks = []
        for cluster in range(k):
            c_vals = np.sort(coeffs[labels == cluster])
            ax.fill_betweenx(
                np.arange(pos, pos + len(c_vals)),
                0, c_vals, alpha=0.7
            )
            ticks.append(pos + len(c_vals)/2)
            pos += len(c_vals) + padding

        ax.yaxis.set_major_locator(FixedLocator(ticks))
        ax.yaxis.set_major_formatter(FixedFormatter(range(k)))
        ax.axvline(x=sil_scores[idx], color='red', linestyle='--')
        ax.set_title(f"{dataset_name} — k={k}")
        if idx % n_cols == 0:
            ax.set_ylabel('Cluster')
        if idx >= (n_rows-1)*n_cols:
            ax.set_xlabel('Silhouette Coefficient')
        else:
            plt.setp(ax.get_xticklabels(), visible=False)

    plt.tight_layout()
    plt.show()

    # store details
    adata.uns['kmeans'][visual]['silhouette_details'] = details
    return dict(zip(k_list, sil_scores))

def plot_kmeans_clusters(
    adata,
    k: int,
    rep_key: str,
    embed: str = 'umap',
    embed_key: str = "",
    pca_dims: tuple = (0, 1),
    random_state: int = 42,
    size: int = 10,
    dataset_name: str = "Dataset",
    consistent_colors: bool = True
):
    """
    Plot KMeans clusters for a single user-specified k value and ground truth conditions side-by-side on UMAP/TSNE or PCA.

    Parameters
    ----------
    adata: AnnData
        Annotated data matrix.
    k: int
        Number of clusters for KMeans.
    rep_key: str
        Key in adata.obsm to cluster on.
    embed: str
        Embedding type ('umap', 'tsne', or 'pca').
    embed_key: str
        Key in adata.obsm for embedding coordinates.
    pca_dims: tuple
        Dimensions to use for PCA plots.
    random_state: int
        Random seed for reproducibility.
    size: int
        Marker size for scatter plots.
    dataset_name: str
        Name of the dataset to include in plot titles.
    consistent_colors: bool
        Whether to use consistent colors across plots for clusters.
    """
    embed_key = embed_key or f'X_{embed}'
    X = adata.obsm.get(rep_key)
    if X is None:
        raise KeyError(f"adata.obsm['{rep_key}'] not found")

    # Run KMeans with user-specified k
    model = KMeans(n_clusters=k, n_init=10, random_state=random_state).fit(X)
    labels_key = f'kmeans_k{k}'
    adata.obs[labels_key] = model.labels_.astype(str)

    if labels_key not in adata.obs:
        raise KeyError(f"adata.obs['{labels_key}'] not found")

    fig, axes = plt.subplots(1, 2, figsize=(12, 6))

    if embed in ('umap', 'tsne'):
        coords = adata.obsm.get(embed_key)
        if coords is None:
            raise KeyError(f"adata.obsm['{embed_key}'] not found")
        # left: KMeans clusters
        ax = axes[0]
        sc.pl.embedding(
            adata,
            basis=embed,
            color=[labels_key],
            title=f'{dataset_name} — {embed.upper()} KMeans (k={k})',
            show=False,
            ax=ax,
            size=size,
            palette="tab20" if consistent_colors else None,
        )
        # right: true condition
        ax = axes[1]
        sc.pl.embedding(
            adata,
            basis=embed,
            color=['condition'],
            title=f'{dataset_name} — {embed.upper()} True Condition',
            show=False,
            ax=ax,
            size=size,
        )
    elif embed == 'pca':
        pcs = adata.obsm.get(rep_key)
        if pcs is None:
            raise KeyError(f"adata.obsm['{rep_key}'] not found for PCA plot")
        x, y = pca_dims
        # left: KMeans
        ax = axes[0]
        scatter = ax.scatter(
            pcs[:, x], pcs[:, y],
            c=adata.obs[labels_key].astype(int),
            cmap='Set2' if consistent_colors else 'viridis',
            s=size, alpha=0.8
        )
        ax.set_xlabel(f'PC{x+1}'), ax.set_ylabel(f'PC{y+1}')
        ax.set_title(f'{dataset_name} — PCA KMeans (k={k}) — PCs {x+1} vs {y+1}')
        handles, _ = scatter.legend_elements()
        ax.legend(handles, [f'Cluster {i}' for i in range(k)], title='Cluster')
        # right: true condition
        ax = axes[1]
        scatter = ax.scatter(
            pcs[:, x], pcs[:, y],
            c=adata.obs['condition'].astype('category').cat.codes,
            cmap='Set2', s=size, alpha=0.8
        )
        ax.set_xlabel(f'PC{x+1}'), ax.set_ylabel(f'PC{y+1}')
        ax.set_title(f'{dataset_name} — PCA True Condition')
        handles, _ = scatter.legend_elements()
        ax.legend(handles, adata.obs['condition'].cat.categories, title='Condition')
    else:
        raise ValueError("embed must be 'umap', 'tsne', or 'pca'")

    plt.tight_layout()
    plt.show()

def evaluate_clustering(true_labels, cluster_labels, method_name="Clustering"):
    """
    Evaluate clustering against ground-truth labels, including confusion matrix
    with both raw counts and row-/column-wise percentages.

    Parameters
    ----------
    true_labels : array-like
        Ground-truth class labels.
    cluster_labels : array-like
        Cluster assignments.
    method_name : str
        Name of the clustering method for printouts.

    Returns
    -------
    results : dict
        {
            'ARI': float,
            'NMI': float,
            'contingency': pd.DataFrame,
            'row_pct': pd.DataFrame,
            'col_pct': pd.DataFrame,
            'purity_per_cluster': pd.Series,
            'overall_purity': float
        }
    """
    # External metrics
    ari = adjusted_rand_score(true_labels, cluster_labels)
    nmi = normalized_mutual_info_score(true_labels, cluster_labels)
    
    # Contingency table
    ct = pd.crosstab(
        pd.Series(true_labels, name="True"),
        pd.Series(cluster_labels, name="Cluster")
    )

    # Percentages
    row_pct = ct.div(ct.sum(axis=1), axis=0) * 100
    col_pct = ct.div(ct.sum(axis=0), axis=1) * 100

    # Purity calculations
    purity_per_cluster = ct.max(axis=0) / ct.sum(axis=0)
    overall_purity = ct.values.max(axis=1).sum() / ct.values.sum()

    # Print results
    print(f"\n=== {method_name} Evaluation ===")
    print(f"ARI: {ari:.4f}    NMI: {nmi:.4f}\n")
    
    print("Contingency Table (raw counts):")
    print(ct, "\n")
    
    print("Row-wise percentages (each true class → clusters):")
    print(row_pct.round(1).astype(str) + "%", "\n")
    
    print("Column-wise percentages (each cluster ← true classes):")
    print(col_pct.round(1).astype(str) + "%", "\n")
    
    print("Purity per cluster:")
    print(purity_per_cluster.to_frame(name="Purity"), "\n")
    print(f"Overall purity: {overall_purity:.4f}\n")

    # Return everything for further inspection
    return {
        'ARI': ari,
        'NMI': nmi,
        'contingency': ct,
        'row_pct': row_pct,
        'col_pct': col_pct,
        'purity_per_cluster': purity_per_cluster,
        'overall_purity': overall_purity
    }

kmeans_optimization(adata_ss_mcf7, visual="pca_95")
silhouette_diagrams(adata_ss_mcf7, k_range=range(2, 7), dataset_name="Smart-seq MCF7", visual="pca_95")

{2: np.float32(0.49936017),
 3: np.float32(0.4744652),
 4: np.float32(0.46488273),
 5: np.float32(0.4413419),
 6: np.float32(0.42580906)}

plot_kmeans_clusters(
    adata_ss_mcf7,
    k = 2,
    embed='umap',
    rep_key='X_pca_95',
    size=30,
    dataset_name="Smart-seq MCF7"
)

print("=========================")
plot_kmeans_clusters(
    adata_ss_mcf7,
    k = 2,
    embed='tsne',
    rep_key='X_pca_95',
    size=30,
    dataset_name="Smart-seq MCF7"
)
print("=========================")

plot_kmeans_clusters(
    adata_ss_mcf7,
    k = 2,
    embed='pca',
    rep_key='X_pca_95',
    pca_dims=(0, 5),
    size=30,
    dataset_name="Smart-seq MCF7"
)

=========================

=========================

evaluate_clustering(
    adata_ss_mcf7.obs['condition'],
    adata_ss_mcf7.obs['kmeans_k2'],
    method_name="Smart-seq MCF7 KMeans (k=2)"
)
print("\n")

=== Smart-seq MCF7 KMeans (k=2) Evaluation ===
ARI: 0.8907    NMI: 0.8430

Contingency Table (raw counts):
Cluster    0    1
True             
Hypo     117    7
Norm       0  126 

Row-wise percentages (each true class → clusters):
Cluster      0       1
True                  
Hypo     94.4%    5.6%
Norm      0.0%  100.0% 

Column-wise percentages (each cluster ← true classes):
Cluster       0      1
True                  
Hypo     100.0%   5.3%
Norm       0.0%  94.7% 

Purity per cluster:
           Purity
Cluster          
0        1.000000
1        0.947368 

Overall purity: 0.9720

kmeans_optimization(adata_ss_hcc, visual="pca_95")
silhouette_diagrams(adata_ss_hcc, k_range=range(2, 8), dataset_name="Smart-seq HCC1806", visual="pca_95")

{2: np.float32(0.2634671),
 3: np.float32(0.16170451),
 4: np.float32(0.16963938),
 5: np.float32(0.18061545),
 6: np.float32(0.190826),
 7: np.float32(0.17456625)}

plot_kmeans_clusters(
    adata_ss_hcc,
    rep_key='X_pca_95',
    k = 2,
    embed='tsne',
    size=50,
    dataset_name="Smart-seq HCC1806"
)
print("==========================")

plot_kmeans_clusters(
    adata_ss_hcc,
    rep_key='X_pca_95',
    k = 2,
    embed='pca',
    pca_dims=(1, 2),
    size=30,
    dataset_name="Smart-seq HCC1806"
)

==========================

evaluate_clustering(
    adata_ss_hcc.obs['condition'],
    adata_ss_hcc.obs['kmeans_k2'],
    method_name="KMeans Clustering (HCC) k=2"
)
print("\n")

=== KMeans Clustering (HCC) k=2 Evaluation ===
ARI: -0.0042    NMI: 0.0000

Contingency Table (raw counts):
Cluster   0   1
True           
Hypo     28  69
Norm     25  60 

Row-wise percentages (each true class → clusters):
Cluster      0      1
True                 
Hypo     28.9%  71.1%
Norm     29.4%  70.6% 

Column-wise percentages (each cluster ← true classes):
Cluster      0      1
True                 
Hypo     52.8%  53.5%
Norm     47.2%  46.5% 

Purity per cluster:
           Purity
Cluster          
0        0.528302
1        0.534884 

Overall purity: 0.7088

plot_kmeans_clusters(
    adata_ss_hcc,
    rep_key='X_pca_95',
    k = 3,
    embed='umap',
    size=50,
    dataset_name="Smart-seq HCC1806"
)
print("==========================")
plot_kmeans_clusters(
    adata_ss_hcc,
    rep_key='X_pca_95',
    k = 3,
    embed='pca',
    pca_dims=(1, 2),
    size=30,
    dataset_name="Smart-seq HCC1806"
)

==========================

evaluate_clustering(
    adata_ss_hcc.obs['condition'],
    adata_ss_hcc.obs['kmeans_k3'],
    method_name="KMeans Clustering (HCC) k =3"
)
print("\n")

=== KMeans Clustering (HCC) k =3 Evaluation ===
ARI: 0.5161    NMI: 0.4844

Contingency Table (raw counts):
Cluster   0   1   2
True               
Hypo     18   7  72
Norm     19  66   0 

Row-wise percentages (each true class → clusters):
Cluster      0      1      2
True                        
Hypo     18.6%   7.2%  74.2%
Norm     22.4%  77.6%   0.0% 

Column-wise percentages (each cluster ← true classes):
Cluster      0      1       2
True                         
Hypo     48.6%   9.6%  100.0%
Norm     51.4%  90.4%    0.0% 

Purity per cluster:
           Purity
Cluster          
0        0.513514
1        0.904110
2        1.000000 

Overall purity: 0.7582

kmeans_optimization(adata_ds_mcf7_scaled, rep_key="X_pca")
silhouette_diagrams(adata_ds_mcf7_scaled, k_range=range(2, 7), dataset_name="Drop-seq MCF7", rep_key="X_pca")

{2: np.float32(0.24377671),
 3: np.float32(0.26212752),
 4: np.float32(0.24349803),
 5: np.float32(0.18707642),
 6: np.float32(0.18642579)}

# scaled version
plot_kmeans_clusters(
    adata_ds_mcf7_scaled,
    rep_key='X_pca',
    embed='tsne',
    size=10,
    k=3,
    dataset_name = "Drop-seq MCF7"
)
print("==========================")

plot_kmeans_clusters(
    adata_ds_mcf7_scaled,
    embed='umap',
    size=10,
    rep_key='X_pca',
    k=3,
    dataset_name="Drop-seq MCF7"
)
print("==========================")

plot_kmeans_clusters(
    adata_ds_mcf7_scaled,
    embed='pca',
    rep_key='X_pca',
    pca_dims=(0,2),
    size=10,
    k=3,
    dataset_name="Drop-seq MCF7"
)

==========================

==========================

evaluate_clustering(
    adata_ds_mcf7_scaled.obs['condition'],
    adata_ds_mcf7_scaled.obs['kmeans_k3'],
    method_name="Drop-seq MCF7 KMeans (k=3)"
)
print("\n")

=== Drop-seq MCF7 KMeans (k=3) Evaluation ===
ARI: 0.3221    NMI: 0.3356

Contingency Table (raw counts):
Cluster     0     1     2
True                     
Hypo     5753  2603   565
Norm      160  9065  3480 

Row-wise percentages (each true class → clusters):
Cluster      0      1      2
True                        
Hypo     64.5%  29.2%   6.3%
Norm      1.3%  71.3%  27.4% 

Column-wise percentages (each cluster ← true classes):
Cluster      0      1      2
True                        
Hypo     97.3%  22.3%  14.0%
Norm      2.7%  77.7%  86.0% 

Purity per cluster:
           Purity
Cluster          
0        0.972941
1        0.776911
2        0.860321 

Overall purity: 0.6852

kmeans_optimization(adata_ds_hcc_scaled, rep_key="X_pca")
silhouette_diagrams(adata_ds_hcc_scaled, k_range=range(2, 8), dataset_name="Drop-seq HCC1806 scaled", rep_key="X_pca")

{2: np.float32(0.2080853),
 3: np.float32(0.17907955),
 4: np.float32(0.17723405),
 5: np.float32(0.1760093),
 6: np.float32(0.17995057),
 7: np.float32(0.16560963)}

#scaled version
plot_kmeans_clusters(
    adata_ds_hcc_scaled,
    rep_key='X_pca',
    embed='umap',
    size=10,
    k=2,
    dataset_name="Drop-seq HCC1806"
)
plot_kmeans_clusters(
    adata_ds_hcc_scaled,
    embed='tsne',
    size=10,
    rep_key='X_pca',
    k=2,
    dataset_name="Drop-seq HCC1806"
)
plot_kmeans_clusters(
    adata_ds_hcc_scaled,
    embed='pca',
    rep_key='X_pca',
    pca_dims=(2,3),
    size=10,
    k=2,
    dataset_name="Drop-seq HCC1806"
)

    # ------------------------------------------    
    # Step 1: Use PCA-reduced data from Scanpy
    # ------------------------------------------
def plot_dendrogram(adata, use_rep='X_pca_95', title="title"):
    # Check if PCA has been computed
    X = adata.obsm[use_rep]  # MCF7 SmartSeq PCA coords

    # ------------------------------------------
    # Step 2: Plot dendrogram to visualize hierarchy
    # ------------------------------------------
    linked = linkage(X, method='ward')

    plt.figure(figsize=(12, 6))
    dendrogram(
        linked,
        orientation='top',
        distance_sort='descending',
        show_leaf_counts=False,
        truncate_mode='level',
        p=30
    )
    plt.title(f'Hierarchical Clustering Dendrogram for {title}')
    plt.xlabel('Cells (truncated)')
    plt.ylabel('Distance')
    plt.grid(True)
    plt.tight_layout()
    plt.show()

    # ------------------------------------------
    # Step 3: Run Agglomerative Clustering
    # ------------------------------------------

def run_agglo(adata, cut, components="1,2", use_rep='X_pca_95', title=""):
    # Check if PCA has been computed
    X = adata.obsm[use_rep]  # MCF7 SmartSeq PCA coords

    # Perform Agglomerative Clustering
    agglo = AgglomerativeClustering(n_clusters=cut, linkage='ward')
    hc_labels = agglo.fit_predict(X)

    adata.obs['hc_clusters'] = hc_labels.astype(str)

    # ------------------------------------------
    # Step 4: Plot PCA and UMAP colored by cluster vs. condition
    # ------------------------------------------
    fig, axes = plt.subplots(2, 2, figsize=(16, 14))

    # Top row: PCA
    sc.pl.pca(
        adata,
        color='hc_clusters',
        ax=axes[0, 0],
        show=False,
        size=50,
        components=components
    )
    axes[0, 0].set_title(f"PCA: Agglomerative Clusters for {title}, {cut} clusters")

    sc.pl.pca(
        adata,
        color='condition',
        ax=axes[0, 1],
        show=False,
        size=50,
        components=components
    )
    axes[0, 1].set_title(f"PCA: Original Condition for {title}, {cut} clusters")
    # Bottom row: UMAP
    sc.pl.umap(
        adata,
        color='hc_clusters',
        ax=axes[1, 0],
        show=False,
        size=50
    )
    axes[1, 0].set_title(f'UMAP: Agglomerative Clusters for {title}, {cut} clusters')

    sc.pl.umap(
        adata,
        color='condition',
        ax=axes[1, 1],
        show=False,
        size=50
    )
    axes[1, 1].set_title(f'UMAP: Original Condition for {title}, {cut} clusters')

    plt.tight_layout()
    plt.show()

plot_dendrogram(adata_ss_mcf7, use_rep='X_pca_95', title="MCF7 SmartSeq")
run_agglo(adata_ss_mcf7, cut=2, components="1,2", use_rep='X_pca_95', title="MCF7 SmartSeq")

evaluate_clustering(
    adata_ss_mcf7.obs['condition'],
    adata_ss_mcf7.obs['hc_clusters'],
    method_name="Smart-seq HCC1806 Agglomerative Clustering (k=2)"
)
print("\n")

=== Smart-seq HCC1806 Agglomerative Clustering (k=2) Evaluation ===
ARI: 0.9368    NMI: 0.8974

Contingency Table (raw counts):
Cluster    0    1
True             
Hypo     124    0
Norm       4  122 

Row-wise percentages (each true class → clusters):
Cluster       0      1
True                  
Hypo     100.0%   0.0%
Norm       3.2%  96.8% 

Column-wise percentages (each cluster ← true classes):
Cluster      0       1
True                  
Hypo     96.9%    0.0%
Norm      3.1%  100.0% 

Purity per cluster:
          Purity
Cluster         
0        0.96875
1        1.00000 

Overall purity: 0.9840

plot_dendrogram(adata_ss_hcc, use_rep='X_pca_95', title="HCC1806 SmartSeq")
run_agglo(adata_ss_hcc, cut=2, components='2,3', use_rep='X_pca_95', title="HCC1806 SmartSeq")

evaluate_clustering(
    adata_ss_hcc.obs['condition'],
    adata_ss_hcc.obs['hc_clusters'],
    method_name="Smart-seq HCC1806 Agglomerative Clustering (k=2)"
)
print("\n")

=== Smart-seq HCC1806 Agglomerative Clustering (k=2) Evaluation ===
ARI: 0.0257    NMI: 0.0201

Contingency Table (raw counts):
Cluster   0   1
True           
Hypo     27  70
Norm     37  48 

Row-wise percentages (each true class → clusters):
Cluster      0      1
True                 
Hypo     27.8%  72.2%
Norm     43.5%  56.5% 

Column-wise percentages (each cluster ← true classes):
Cluster      0      1
True                 
Hypo     42.2%  59.3%
Norm     57.8%  40.7% 

Purity per cluster:
           Purity
Cluster          
0        0.578125
1        0.593220 

Overall purity: 0.6484

run_agglo(adata_ss_hcc, cut=6, components='2,3', use_rep='X_pca_95', title="HCC1806 SmartSeq")

evaluate_clustering(
    adata_ss_hcc.obs['condition'],
    adata_ss_hcc.obs['hc_clusters'],
    method_name="Smart-seq HCC1806 Agglomerative Clustering (k=6)"
)
print("\n")

=== Smart-seq HCC1806 Agglomerative Clustering (k=6) Evaluation ===
ARI: 0.3229    NMI: 0.3799

Contingency Table (raw counts):
Cluster   0  1  2   3   4  5
True                        
Hypo     25  1  8   4  58  1
Norm     36  1  0  48   0  0 

Row-wise percentages (each true class → clusters):
Cluster      0     1     2      3      4     5
True                                          
Hypo     25.8%  1.0%  8.2%   4.1%  59.8%  1.0%
Norm     42.4%  1.2%  0.0%  56.5%   0.0%  0.0% 

Column-wise percentages (each cluster ← true classes):
Cluster      0      1       2      3       4       5
True                                                
Hypo     41.0%  50.0%  100.0%   7.7%  100.0%  100.0%
Norm     59.0%  50.0%    0.0%  92.3%    0.0%    0.0% 

Purity per cluster:
           Purity
Cluster          
0        0.590164
1        0.500000
2        1.000000
3        0.923077
4        1.000000
5        1.000000 

Overall purity: 0.5824

plot_dendrogram(adata_ds_mcf7_scaled, use_rep='X_pca', title="MCF7 DropSeq")

run_agglo(adata_ds_mcf7_scaled, cut=2, components='1,3', use_rep='X_pca', title="MCF7 DropSeq")

evaluate_clustering(
    adata_ds_mcf7_scaled.obs['condition'],
    adata_ds_mcf7_scaled.obs['hc_clusters'],
    method_name="Drop-seq MCF7 Agglomerative Clustering (k=2)"
)
print("\n")

=== Drop-seq MCF7 Agglomerative Clustering (k=2) Evaluation ===
ARI: 0.5094    NMI: 0.4794

Contingency Table (raw counts):
Cluster      0     1
True                
Hypo      2984  5937
Norm     12616    89 

Row-wise percentages (each true class → clusters):
Cluster      0      1
True                 
Hypo     33.4%  66.6%
Norm     99.3%   0.7% 

Column-wise percentages (each cluster ← true classes):
Cluster      0      1
True                 
Hypo     19.1%  98.5%
Norm     80.9%   1.5% 

Purity per cluster:
           Purity
Cluster          
0        0.808718
1        0.985231 

Overall purity: 0.8579

run_agglo(adata_ds_mcf7_scaled, cut=3, components='1,3', use_rep='X_pca' , title="MCF7 DropSeq")

evaluate_clustering(
    adata_ds_mcf7_scaled.obs['condition'],
    adata_ds_mcf7_scaled.obs['hc_clusters'],
    method_name="Drop-seq MCF7 Agglomerative Clustering (k=3)"
)
print("\n")

=== Drop-seq MCF7 Agglomerative Clustering (k=3) Evaluation ===
ARI: 0.3801    NMI: 0.4052

Contingency Table (raw counts):
Cluster      0     1     2
True                      
Hypo      2906  5937    78
Norm     10479    89  2137 

Row-wise percentages (each true class → clusters):
Cluster      0      1      2
True                        
Hypo     32.6%  66.6%   0.9%
Norm     82.5%   0.7%  16.8% 

Column-wise percentages (each cluster ← true classes):
Cluster      0      1      2
True                        
Hypo     21.7%  98.5%   3.5%
Norm     78.3%   1.5%  96.5% 

Purity per cluster:
           Purity
Cluster          
0        0.782891
1        0.985231
2        0.964786 

Overall purity: 0.7591

plot_dendrogram(adata_ds_hcc_scaled, use_rep="X_pca", title="HCC1806 DropSeq")

run_agglo(adata_ds_hcc_scaled, cut=2, components="3,4", use_rep='X_pca', title="HCC1806 DropSeq")

evaluate_clustering(
    adata_ds_hcc_scaled.obs['condition'],
    adata_ds_hcc_scaled.obs['hc_clusters'],
    method_name="Drop-seq HCC1806 Agglomerative Clustering (k=2)"
)
print("\n")

=== Drop-seq HCC1806 Agglomerative Clustering (k=2) Evaluation ===
ARI: 0.0640    NMI: 0.0364

Contingency Table (raw counts):
Cluster     0     1
True               
Hypo     6186  2713
Norm     2741  3042 

Row-wise percentages (each true class → clusters):
Cluster      0      1
True                 
Hypo     69.5%  30.5%
Norm     47.4%  52.6% 

Column-wise percentages (each cluster ← true classes):
Cluster      0      1
True                 
Hypo     69.3%  47.1%
Norm     30.7%  52.9% 

Purity per cluster:
           Purity
Cluster          
0        0.692954
1        0.528584 

Overall purity: 0.6285

run_agglo(adata_ds_hcc_scaled, cut=4, components="3,4", use_rep='X_pca', title="HCC1806 DropSeq")

evaluate_clustering(
    adata_ds_hcc_scaled.obs['condition'],
    adata_ds_hcc_scaled.obs['hc_clusters'],
    method_name="Drop-seq HCC1806 Agglomerative Clustering (k=4)"
)
print("\n")

=== Drop-seq HCC1806 Agglomerative Clustering (k=4) Evaluation ===
ARI: 0.2156    NMI: 0.2104

Contingency Table (raw counts):
Cluster     0     1     2     3
True                           
Hypo      998  2216  5188   497
Norm     1634   210  1107  2832 

Row-wise percentages (each true class → clusters):
Cluster      0      1      2      3
True                               
Hypo     11.2%  24.9%  58.3%   5.6%
Norm     28.3%   3.6%  19.1%  49.0% 

Column-wise percentages (each cluster ← true classes):
Cluster      0      1      2      3
True                               
Hypo     37.9%  91.3%  82.4%  14.9%
Norm     62.1%   8.7%  17.6%  85.1% 

Purity per cluster:
           Purity
Cluster          
0        0.620821
1        0.913438
2        0.824146
3        0.850706 

Overall purity: 0.5462

# Using the igraph implementation and a fixed number of iterations can be significantly faster, especially for larger datasets
sc.tl.leiden(adata_ss_mcf7, flavor="igraph", n_iterations=2, random_state=42, resolution=0.1)
sc.pl.umap(
    adata_ss_mcf7,
    color=['leiden', 'condition'],
    show=False,
    size=40,
    title=["Leiden Clustering: MCF7 Smart-seq", "Condition for MCF7 Smart-seq"]
)

[<Axes: title={'center': 'Leiden Clustering: MCF7 Smart-seq'}, xlabel='UMAP1', ylabel='UMAP2'>,
 <Axes: title={'center': 'Condition for MCF7 Smart-seq'}, xlabel='UMAP1', ylabel='UMAP2'>]

#plot also in the pca space
sc.pl.pca(
    adata_ss_mcf7,
    color=['leiden', 'condition'],
    show=False,
    size=40,
    components="1,6",
    title=["Leiden Clustering: MCF7 Drop-seq scaled", "Condition for MCF7 Drop-seq scaled"]
)

[<Axes: title={'center': 'Leiden Clustering: MCF7 Drop-seq scaled'}, xlabel='PC1', ylabel='PC6'>,
 <Axes: title={'center': 'Condition for MCF7 Drop-seq scaled'}, xlabel='PC1', ylabel='PC6'>]

evaluate_clustering(
    adata_ss_mcf7.obs['condition'],
    adata_ss_mcf7.obs['leiden'],
    method_name="Smart-seq MCF7 Agglomerative Clustering"
)
print("\n")

=== Smart-seq MCF7 Agglomerative Clustering Evaluation ===
ARI: 0.9681    NMI: 0.9407

Contingency Table (raw counts):
Cluster    0    1
True             
Hypo       2  122
Norm     126    0 

Row-wise percentages (each true class → clusters):
Cluster       0      1
True                  
Hypo       1.6%  98.4%
Norm     100.0%   0.0% 

Column-wise percentages (each cluster ← true classes):
Cluster      0       1
True                  
Hypo      1.6%  100.0%
Norm     98.4%    0.0% 

Purity per cluster:
           Purity
Cluster          
0        0.984375
1        1.000000 

Overall purity: 0.9920

sc.tl.leiden(adata_ss_hcc, flavor="igraph", n_iterations=2, random_state=42, resolution=0.3)
sc.pl.umap(
    adata_ss_hcc,
    color=['leiden', 'condition'],
    show=False,
    size=40,
    title=["Leiden Clustering: HCC1806 Smart-seq", "Condition for HCC1806 Smart-seq"]
)

[<Axes: title={'center': 'Leiden Clustering: HCC1806 Smart-seq'}, xlabel='UMAP1', ylabel='UMAP2'>,
 <Axes: title={'center': 'Condition for HCC1806 Smart-seq'}, xlabel='UMAP1', ylabel='UMAP2'>]

#plot also in the pca space
sc.pl.pca(
    adata_ss_hcc,
    color=['leiden', 'condition'],
    show=False,
    size=40,
    components="2,3",
    title=["Leiden Clustering: HCC Drop-seq scaled", "Condition for HCC Drop-seq scaled"]
)

[<Axes: title={'center': 'Leiden Clustering: HCC Drop-seq scaled'}, xlabel='PC2', ylabel='PC3'>,
 <Axes: title={'center': 'Condition for HCC Drop-seq scaled'}, xlabel='PC2', ylabel='PC3'>]

evaluate_clustering(
    adata_ss_hcc.obs['condition'],
    adata_ss_hcc.obs['leiden'],
    method_name="Smart-seq HCC1806"
)
print("\n")

=== Smart-seq HCC1806 Evaluation ===
ARI: 0.8109    NMI: 0.7390

Contingency Table (raw counts):
Cluster   0   1
True           
Hypo      8  89
Norm     84   1 

Row-wise percentages (each true class → clusters):
Cluster      0      1
True                 
Hypo      8.2%  91.8%
Norm     98.8%   1.2% 

Column-wise percentages (each cluster ← true classes):
Cluster      0      1
True                 
Hypo      8.7%  98.9%
Norm     91.3%   1.1% 

Purity per cluster:
           Purity
Cluster          
0        0.913043
1        0.988889 

Overall purity: 0.9505

# scaled version
sc.tl.leiden(adata_ds_mcf7_scaled, flavor="igraph", n_iterations=2, random_state=42, resolution=0.1)
sc.pl.umap(
    adata_ds_mcf7_scaled,
    color=['leiden', 'condition'],
    show=False,
    size=6,
    title=["Leiden Clustering: MCF7 Drop-seq scaled", "Condition for MCF7 Drop-seq scaled"]
)

[<Axes: title={'center': 'Leiden Clustering: MCF7 Drop-seq scaled'}, xlabel='UMAP1', ylabel='UMAP2'>,
 <Axes: title={'center': 'Condition for MCF7 Drop-seq scaled'}, xlabel='UMAP1', ylabel='UMAP2'>]

#plot also in the pca space
sc.pl.pca(
    adata_ds_mcf7_scaled,
    color=['leiden', 'condition'],
    show=False,
    size=20,
    components="1,3",
    title=["Leiden Clustering: MCF7 Drop-seq scaled", "Condition for MCF7 Drop-seq scaled"]
)

[<Axes: title={'center': 'Leiden Clustering: MCF7 Drop-seq scaled'}, xlabel='PC1', ylabel='PC3'>,
 <Axes: title={'center': 'Condition for MCF7 Drop-seq scaled'}, xlabel='PC1', ylabel='PC3'>]

evaluate_clustering(
    adata_ds_mcf7_scaled.obs['condition'],
    adata_ds_mcf7_scaled.obs['leiden'],
    method_name=" Drop-seq MCF7 Leiden Clustering"
)
print("\n")

===  Drop-seq MCF7 Leiden Clustering Evaluation ===
ARI: 0.9090    NMI: 0.8449

Contingency Table (raw counts):
Cluster      0     1
True                
Hypo       417  8504
Norm     12619    86 

Row-wise percentages (each true class → clusters):
Cluster      0      1
True                 
Hypo      4.7%  95.3%
Norm     99.3%   0.7% 

Column-wise percentages (each cluster ← true classes):
Cluster      0      1
True                 
Hypo      3.2%  99.0%
Norm     96.8%   1.0% 

Purity per cluster:
           Purity
Cluster          
0        0.968012
1        0.989988 

Overall purity: 0.9767

sc.tl.leiden(adata_ds_hcc_scaled, flavor="igraph", n_iterations=2, random_state=42, resolution=0.16)
sc.pl.umap(
    adata_ds_hcc_scaled,
    color=['leiden', 'condition'],
    show=False,
    size=20,
    title=["Leiden Clustering: HCC1806 Drop-seq scaled", "Condition for HCC1806 Drop-seq scaled"]
)

[<Axes: title={'center': 'Leiden Clustering: HCC1806 Drop-seq scaled'}, xlabel='UMAP1', ylabel='UMAP2'>,
 <Axes: title={'center': 'Condition for HCC1806 Drop-seq scaled'}, xlabel='UMAP1', ylabel='UMAP2'>]

#plot also in the pca space
sc.pl.pca(
    adata_ds_hcc_scaled,
    color=['leiden', 'condition'],
    show=False,
    size=20,
    components="3,4",
    title=["Leiden Clustering: HCC Drop-seq scaled", "Condition for HCC Drop-seq scaled"]
)

[<Axes: title={'center': 'Leiden Clustering: HCC Drop-seq scaled'}, xlabel='PC3', ylabel='PC4'>,
 <Axes: title={'center': 'Condition for HCC Drop-seq scaled'}, xlabel='PC3', ylabel='PC4'>]

evaluate_clustering(
    adata_ds_hcc_scaled.obs['condition'],
    adata_ds_hcc_scaled.obs['leiden'],
    method_name="Smart-seq HCC1806 Leiden Clustering"
)
print("\n")

=== Smart-seq HCC1806 Leiden Clustering Evaluation ===
ARI: 0.7775    NMI: 0.6787

Contingency Table (raw counts):
Cluster     0     1
True               
Hypo      634  8265
Norm     5550   233 

Row-wise percentages (each true class → clusters):
Cluster      0      1
True                 
Hypo      7.1%  92.9%
Norm     96.0%   4.0% 

Column-wise percentages (each cluster ← true classes):
Cluster      0      1
True                 
Hypo     10.3%  97.3%
Norm     89.7%   2.7% 

Purity per cluster:
           Purity
Cluster          
0        0.897477
1        0.972582 

Overall purity: 0.9409

X_pca_ss_mcf7 = adata_ss_mcf7.obsm['X_pca_95']
X_pca_ss_hcc = adata_ss_hcc.obsm['X_pca_95']
X_pca_ds_mcf7 = adata_ds_mcf7.obsm['X_pca_95']
X_pca_ds_hcc = adata_ds_hcc.obsm['X_pca_95']

y_pca_ss_mcf7 = adata_ss_mcf7.obs['condition'].values
y_pca_ss_hcc = adata_ss_hcc.obs['condition'].values
y_pca_ds_mcf7 = adata_ds_mcf7.obs['condition'].values
y_pca_ds_hcc = adata_ds_hcc.obs['condition'].values

print(
    f"SmartSeq MCF7 X_pca shape: {X_pca_ss_mcf7.shape}, y_pca shape: {y_pca_ss_mcf7.shape}",
    f"SmartSeq HCC1806 X_pca shape: {X_pca_ss_hcc.shape}, y_pca shape: {y_pca_ss_hcc.shape}",
    f"DropSeq MCF7 X_pca shape: {X_pca_ds_mcf7.shape}, y_pca shape: {y_pca_ds_mcf7.shape}",
    f"DropSeq HCC1806 X_pca shape: {X_pca_ds_hcc.shape}, y_pca shape: {y_pca_ds_hcc.shape}",
    sep = "\n"
)

SmartSeq MCF7 X_pca shape: (250, 20), y_pca shape: (250,)
SmartSeq HCC1806 X_pca shape: (182, 34), y_pca shape: (182,)
DropSeq MCF7 X_pca shape: (21626, 761), y_pca shape: (21626,)
DropSeq HCC1806 X_pca shape: (14682, 844), y_pca shape: (14682,)

encoder = LabelEncoder()
y_pca_ss_mcf7_encoded = encoder.fit_transform(y_pca_ss_mcf7)
y_pca_ss_hcc_encoded = encoder.fit_transform(y_pca_ss_hcc)
y_pca_ds_mcf7_encoded = encoder.fit_transform(y_pca_ds_mcf7)
y_pca_ds_hcc_encoded = encoder.fit_transform(y_pca_ds_hcc)

print("Label classes:", encoder.classes_)
print("Internal encoding:", encoder.transform(encoder.classes_))

Label classes: ['Hypo' 'Norm']
Internal encoding: [0 1]

print("SmartSeq MCF7:", np.unique(y_pca_ss_mcf7, return_counts = True))
print("SmartSeq HCC:", np.unique(y_pca_ss_hcc, return_counts = True))
print("DropSeq MCF7:", np.unique(y_pca_ds_mcf7, return_counts = True))
print("DropSeq HCC:", np.unique(y_pca_ds_hcc, return_counts = True))

SmartSeq MCF7: (array(['Hypo', 'Norm'], dtype=object), array([124, 126]))
SmartSeq HCC: (array(['Hypo', 'Norm'], dtype=object), array([97, 85]))
DropSeq MCF7: (array(['Hypo', 'Norm'], dtype=object), array([ 8921, 12705]))
DropSeq HCC: (array(['Hypo', 'Norm'], dtype=object), array([8899, 5783]))

def summarize_crossvalidation(search: GridSearchCV | RandomizedSearchCV):
    """Summarize model data for cross-validation."""
    best_model = search.best_estimator_
    
    print("Best Parameters:", search.best_params_)
    print("Best Score (CV avg):", search.best_score_)
    
    attributes = {
        "C": "C",
        # Logistic regression
        "penalty": "Penalty",
        # SVM
        "intercept_": "Intercept",
        "max_iter": "Max Iterations",
        "n_iter_": "Number of iterations for convergence",
        # Random forest
        "n_estimators": "Number of decision trees",
        "max_depth": "Maximum tree depth",
        "min_samples_split": "Minimum samples to split",
        "min_samples_leaf": "Minimum samples per leaf",
        "max_features": "Maximum features considered at each split",
        "bootstrap": "Bootstrap",
        "feature_importances_": "Feature importances",
    }
    
    for attribute, name in attributes.items():
        if hasattr(best_model, attribute):
            print(f"{name}:", getattr(best_model, attribute))

def plot_learning_curve(
    search: GridSearchCV | RandomizedSearchCV,
    param_names: str | list[str],
    plot_title: str = "Learning Curve",
    scoring_label: str | None = None,
    log_scale_params: list[str] | None = None
):
    if not isinstance(param_names, list):
        param_names = [param_names]
    if log_scale_params is None:
        log_scale_params = []
    
    results = search.cv_results_
    n_params = len(param_names)
    
    # Adjust figure size depending on number of subplots
    fig, axes = plt.subplots(n_params, 1, figsize = (10, 4 * n_params), squeeze = False)
    
    if scoring_label is None:
        scoring_label = search.scoring if isinstance(search.scoring, str) else "score"
        
    for i, param in enumerate(param_names):
        raw_values = [params[param] for params in results["params"]]

        # Detect type: numeric or not
        if all(isinstance(val, (int, float)) for val in raw_values):
            param_range = np.array(raw_values)
            unique_param_range = np.unique(param_range)
            is_numeric = True
        else:
            param_range = [str(val) for val in raw_values]
            unique_param_range = sorted(set(param_range))
            is_numeric = False

        train_scores = []
        val_scores = []
        std_scores = []

        for value in unique_param_range:
            if is_numeric:
                mask = param_range == value
            else:
                mask = [v == value for v in param_range]

            train_scores.append(np.mean(np.array(results["mean_train_score"])[mask]))
            val_scores.append(np.mean(np.array(results["mean_test_score"])[mask]))
            std_scores.append(np.mean(np.array(results["std_test_score"])[mask]))

        axis = axes[i, 0]
        x_values = unique_param_range if is_numeric else range(len(unique_param_range))

        # Plot training scores on left y-axis
        axis.plot(x_values, train_scores, label = "Training score", marker = "o", color = "tab:blue")
        axis.set_ylabel(f"Train {scoring_label}", color = "tab:blue")
        axis.tick_params(axis = "y", labelcolor = "tab:blue")

        # Plot validation scores on right y-axis
        axis2 = axis.twinx()
        axis2.plot(x_values, val_scores, label = "Validation score", marker = "s", color = "tab:orange")
        axis2.fill_between(
            x_values,
            np.array(val_scores) - np.array(std_scores),
            np.array(val_scores) + np.array(std_scores),
            alpha = 0.2,
            color = "tab:orange"
        )
        axis2.set_ylabel(f"Validation {scoring_label}", color = "tab:orange")
        axis2.tick_params(axis = "y", labelcolor = "tab:orange")

        axis.set_title(f"{plot_title} ({param})")
        axis.set_xlabel(param)
        axis.grid(True)

        if param in log_scale_params:
            axis.set_xscale("log")
            axis2.set_xscale("log")

        if not is_numeric:
            axis.set_xticks(x_values)
            axis.set_xticklabels(unique_param_range, rotation = 45)

    plt.tight_layout()
    plt.show()

def test_model(model, X_test, y_test, verbose: bool = True):
    if verbose:
        print("========================= Testing =========================")
    predictions = model.predict(X_test)
    accuracy = accuracy_score(y_test, predictions)
    
    if verbose:
        cm = confusion_matrix(y_test, predictions)
        labels = ['Hypo', 'Norm']
        cm_df = pd.DataFrame(cm, index=[f"Actual {l}" for l in labels], columns = [f"Predicted {l}" for l in labels])
        print("Confusion matrix:")
        print(cm_df)
        print("Accuracy:", accuracy)
        print("Classification report:\n", classification_report(y_test, predictions))
    
    return accuracy

class TrainedModelWrapper:
    def __init__(
        self,
        model: BaseEstimator,
        X,
        y,
        X_train,
        y_train,
        X_test,
        y_test,
        accuracy: float
    ):
        self.model = model
        self.X = X
        self.y = y
        self.X_train = X_train
        self.y_train = y_train
        self.X_test = X_test
        self.y_test = y_test
        self.accuracy = accuracy
    
    def predict(self, X):
        return self.model.predict(X)
    
    def score(self, X, y):
        return self.model.score(X, y)

    def summary(self, verbose = True):
        if verbose:
            print("Model:", type(self.model).__name__)
            print("Training set size:", self.X_train.shape)
            print("Test set size:", self.X_test.shape)
            print("Accuracy on test set:", self.accuracy)

def train_logistic_regression(
    X_train,
    y_train,
    random_state: int | None = None,
    n_jobs: int | None = None,
    verbose: bool = True
):
    if verbose:
        print("========================= Training =========================")
    
    n_samples = X_train.shape[0]
    
    params = {
        "penalty": ["l2"],
        "C": [0.01, 0.1, 1, 10],
    } if n_samples < 10_000 else {
        "penalty": ["l2"],
        "C": [0.01, 0.1, 1, 10],
        "solver": ["sag"] # More efficient on larger data sets
    }
    
    model = GridSearchCV(
        estimator = LogisticRegression(max_iter = 20_000, random_state = random_state, n_jobs = n_jobs),
        param_grid = params,
        refit = True,
        cv = 5,
        n_jobs = n_jobs,
        return_train_score = True
    ) if n_samples < 10_000 else RandomizedSearchCV(
        estimator = LogisticRegression(max_iter = 20_000, random_state = random_state, n_jobs = n_jobs),
        param_distributions = params,
        random_state = random_state,
        refit = True,
        cv = 5,
        n_jobs = n_jobs,
        return_train_score = True
    )
    
    model.fit(X_train, y_train)
    
    if verbose:
        summarize_crossvalidation(model)
        print("Training accuracy:", model.score(X_train, y_train))
    
    return model.best_estimator_

def train_test_logistic_regression(
    X,
    y,
    test_size: float = 0.25,
    train_size: float | None = None,
    random_state: int = 10,
    n_jobs: int | None = None,
    verbose: bool = True
):
    # Split the data
    X_train, X_test, y_train, y_test = train_test_split(
        X, y, test_size = test_size, train_size = train_size, random_state = random_state, stratify = y
    )
    
    if verbose:
        print("Training data dimensions:", X_train.shape)
        print("Testing data dimensions:", X_test.shape)
    
    # Train the model
    model = train_logistic_regression(X_train = X_train, y_train = y_train, random_state = random_state, n_jobs = n_jobs, verbose = verbose)
    
    # Evaluate the model
    accuracy = test_model(model = model, X_test = X_test, y_test = y_test, verbose = verbose)
    
    return TrainedModelWrapper(
        model = model,
        X = X,
        y = y,
        X_train = X_train,
        y_train = y_train,
        X_test = X_test,
        y_test = y_test,
        accuracy = accuracy
    )

ss_mcf7_pca_logit = train_test_logistic_regression(X_pca_ss_mcf7, y_pca_ss_mcf7, n_jobs = -1)

Training data dimensions: (187, 20)
Testing data dimensions: (63, 20)
========================= Training =========================
Best Parameters: {'C': 0.01, 'penalty': 'l2'}
Best Score (CV avg): 0.9891891891891891
C: 0.01
Penalty: l2
Intercept: [-8.10718121e-07]
Max Iterations: 10000
Number of iterations for convergence: [35]
Training accuracy: 1.0
========================= Testing =========================
Confusion matrix:
             Predicted Hypo  Predicted Norm
Actual Hypo              31               0
Actual Norm               0              32
Accuracy: 1.0
Classification report:
               precision    recall  f1-score   support

        Hypo       1.00      1.00      1.00        31
        Norm       1.00      1.00      1.00        32

    accuracy                           1.00        63
   macro avg       1.00      1.00      1.00        63
weighted avg       1.00      1.00      1.00        63

ss_hcc_pca_logit = train_test_logistic_regression(X_pca_ss_hcc, y_pca_ss_hcc, n_jobs = -1)

Training data dimensions: (136, 34)
Testing data dimensions: (46, 34)
========================= Training =========================
Best Parameters: {'C': 0.01, 'penalty': 'l2'}
Best Score (CV avg): 0.978042328042328
C: 0.01
Penalty: l2
Intercept: [-1.46755599e-06]
Max Iterations: 10000
Number of iterations for convergence: [46]
Training accuracy: 1.0
========================= Testing =========================
Confusion matrix:
             Predicted Hypo  Predicted Norm
Actual Hypo              24               1
Actual Norm               0              21
Accuracy: 0.9782608695652174
Classification report:
               precision    recall  f1-score   support

        Hypo       1.00      0.96      0.98        25
        Norm       0.95      1.00      0.98        21

    accuracy                           0.98        46
   macro avg       0.98      0.98      0.98        46
weighted avg       0.98      0.98      0.98        46

ds_mcf7_pca_logit = train_test_logistic_regression(X_pca_ds_mcf7, y_pca_ds_mcf7, n_jobs = -1)

Training data dimensions: (16219, 761)
Testing data dimensions: (5407, 761)
========================= Training =========================
Best Parameters: {'solver': 'sag', 'penalty': 'l2', 'C': 0.1}
Best Score (CV avg): 0.9792835217881786
C: 0.1
Penalty: l2
Intercept: [1.]
Max Iterations: 20000
Number of iterations for convergence: [20000]
Training accuracy: 0.9901966829027684
========================= Testing =========================
Confusion matrix:
             Predicted Hypo  Predicted Norm
Actual Hypo            2166              64
Actual Norm              46            3131
Accuracy: 0.9796560014795636
Classification report:
               precision    recall  f1-score   support

        Hypo       0.98      0.97      0.98      2230
        Norm       0.98      0.99      0.98      3177

    accuracy                           0.98      5407
   macro avg       0.98      0.98      0.98      5407
weighted avg       0.98      0.98      0.98      5407

ds_hcc_pca_logit = train_test_logistic_regression(X_pca_ds_hcc, y_pca_ds_hcc, n_jobs = -1)

Training data dimensions: (11011, 844)
Testing data dimensions: (3671, 844)
========================= Training =========================

Best Parameters: {'solver': 'sag', 'penalty': 'l2', 'C': 0.1}
Best Score (CV avg): 0.9554083833332715
C: 0.1
Penalty: l2
Intercept: [-2.1879232]
Max Iterations: 20000
Number of iterations for convergence: [132]
Training accuracy: 0.9741167922986105
========================= Testing =========================
Confusion matrix:
             Predicted Hypo  Predicted Norm
Actual Hypo            2116             109
Actual Norm              80            1366
Accuracy: 0.9485153909016617
Classification report:
               precision    recall  f1-score   support

        Hypo       0.96      0.95      0.96      2225
        Norm       0.93      0.94      0.94      1446

    accuracy                           0.95      3671
   macro avg       0.94      0.95      0.95      3671
weighted avg       0.95      0.95      0.95      3671

def train_svm(
    X_train,
    y_train,
    random_state: int | None = None,
    n_jobs: int | None = None,
    verbose: bool = True
):
    if verbose:
        print("========================= Training =========================")
        
    params = {
        "C": [0.025, 0.05, 0.1, 1, 10, 50]
    }
    
    model = GridSearchCV(
        estimator = LinearSVC(random_state = random_state, max_iter = 10_000),
        param_grid = params,
        refit = True,
        cv = 5,
        n_jobs = n_jobs,
        return_train_score = True
    )
    
    model.fit(X_train, y_train)
    
    if verbose:
        summarize_crossvalidation(model)
        print("Training accuracy:", model.score(X_train, y_train))
        
        plot_learning_curve(model, list(params.keys()))
    
    return model.best_estimator_

def train_test_svm(
    X,
    y,
    test_size: float = 0.25,
    train_size: float | None = None,
    random_state: int = 10,
    n_jobs: int | None = None,
    verbose: bool = True
):
    # Split the data
    X_train, X_test, y_train, y_test = train_test_split(
        X, y, test_size = test_size, train_size = train_size, random_state = random_state, stratify = y
    )
    
    if verbose:
        print("Training data dimensions:", X_train.shape)
        print("Testing data dimensions:", X_test.shape)
    
    # Train the model
    model = train_svm(X_train = X_train, y_train = y_train, random_state = random_state, n_jobs = n_jobs, verbose = verbose)
    
    # Evaluate the model
    accuracy = test_model(model = model, X_test = X_test, y_test = y_test, verbose = verbose)
    
    return TrainedModelWrapper(
        model = model,
        X = X,
        y = y,
        X_train = X_train,
        y_train = y_train,
        X_test = X_test,
        y_test = y_test,
        accuracy = accuracy
    )

ss_mcf7_pca_svm = train_test_svm(X_pca_ss_mcf7, y_pca_ss_mcf7, n_jobs = -1)

Training data dimensions: (187, 20)
Testing data dimensions: (63, 20)
========================= Training =========================
Best Parameters: {'C': 0.025}
Best Score (CV avg): 0.9891891891891891
C: 0.025
Penalty: l2
Intercept: [-1.68452782e-08]
Max Iterations: 10000
Number of iterations for convergence: 8
Training accuracy: 1.0

========================= Testing =========================
Confusion matrix:
             Predicted Hypo  Predicted Norm
Actual Hypo              31               0
Actual Norm               0              32
Accuracy: 1.0
Classification report:
               precision    recall  f1-score   support

        Hypo       1.00      1.00      1.00        31
        Norm       1.00      1.00      1.00        32

    accuracy                           1.00        63
   macro avg       1.00      1.00      1.00        63
weighted avg       1.00      1.00      1.00        63

ss_hcc_pca_svm = train_test_svm(X_pca_ss_hcc, y_pca_ss_hcc, n_jobs = -1)

Training data dimensions: (136, 34)
Testing data dimensions: (46, 34)
========================= Training =========================
Best Parameters: {'C': 0.025}
Best Score (CV avg): 0.9634920634920634
C: 0.025
Penalty: l2
Intercept: [-1.02120292e-07]
Max Iterations: 10000
Number of iterations for convergence: 9
Training accuracy: 1.0

========================= Testing =========================
Confusion matrix:
             Predicted Hypo  Predicted Norm
Actual Hypo              24               1
Actual Norm               0              21
Accuracy: 0.9782608695652174
Classification report:
               precision    recall  f1-score   support

        Hypo       1.00      0.96      0.98        25
        Norm       0.95      1.00      0.98        21

    accuracy                           0.98        46
   macro avg       0.98      0.98      0.98        46
weighted avg       0.98      0.98      0.98        46

ds_mcf7_pca_svm = train_test_svm(X_pca_ds_mcf7, y_pca_ds_mcf7, n_jobs = -1)

Training data dimensions: (16219, 761)
Testing data dimensions: (5407, 761)
========================= Training =========================
Best Parameters: {'C': 0.025}
Best Score (CV avg): 0.9773103826395694
C: 0.025
Penalty: l2
Intercept: [0.32791924]
Max Iterations: 10000
Number of iterations for convergence: 8
Training accuracy: 0.9903199950675134

========================= Testing =========================
Confusion matrix:
             Predicted Hypo  Predicted Norm
Actual Hypo            2163              67
Actual Norm              51            3126
Accuracy: 0.9781764379508046
Classification report:
               precision    recall  f1-score   support

        Hypo       0.98      0.97      0.97      2230
        Norm       0.98      0.98      0.98      3177

    accuracy                           0.98      5407
   macro avg       0.98      0.98      0.98      5407
weighted avg       0.98      0.98      0.98      5407

ds_hcc_pca_svm = train_test_svm(X_pca_ds_hcc, y_pca_ds_hcc, n_jobs = -1)

Training data dimensions: (11011, 844)
Testing data dimensions: (3671, 844)
========================= Training =========================
Best Parameters: {'C': 0.025}
Best Score (CV avg): 0.9528651995070714
C: 0.025
Penalty: l2
Intercept: [-0.82237322]
Max Iterations: 10000
Number of iterations for convergence: 8
Training accuracy: 0.9789301607483426

========================= Testing =========================
Confusion matrix:
             Predicted Hypo  Predicted Norm
Actual Hypo            2112             113
Actual Norm              82            1364
Accuracy: 0.9468809588667938
Classification report:
               precision    recall  f1-score   support

        Hypo       0.96      0.95      0.96      2225
        Norm       0.92      0.94      0.93      1446

    accuracy                           0.95      3671
   macro avg       0.94      0.95      0.94      3671
weighted avg       0.95      0.95      0.95      3671

def train_random_forest(
    X_train,
    y_train,
    random_state: int | None = None,
    n_jobs: int | None = None,
    verbose: bool = True
):  
    if verbose:
        print("========================= Training =========================")
    n_samples = X_train.shape[0]
    
    params = {
        "n_estimators": [25, 50, 100],
        "max_depth": [5, 10, 20, None],
        "min_samples_split": [2, 5, 10],
        "min_samples_leaf": [1, 2, 4, 10],
        "max_features": ["sqrt", "log2", None],
        "bootstrap": [True, False]
    } if n_samples < 1_000 else {
        "n_estimators": [100, 200, 300, 400],
        "class_weight": ["balanced"],
        "max_depth": [5, 10, 20],
        "min_samples_split": [5, 10, 20],
        "min_samples_leaf": [1, 2, 5, 10, 25],
        "max_features": ["sqrt"],
        "bootstrap": [True]
    } if n_samples < 15_000 else {
        "n_estimators": [100, 200, 400, 600],
        "class_weight": ["balanced"],
        "max_depth": [10, 20, 30],
        "min_samples_split": [2, 5, 10],
        "min_samples_leaf": [1, 2, 4],
        "max_features": ["sqrt"],
        "bootstrap": [True]
    }
    
    model = GridSearchCV(
        estimator = RandomForestClassifier(random_state = random_state, n_jobs = n_jobs),
        param_grid = params,
        scoring = "f1_macro",
        refit = True,
        cv = 5,
        n_jobs = n_jobs,
        return_train_score = True
    ) if n_samples < 10_000 else RandomizedSearchCV(
        estimator = RandomForestClassifier(random_state = random_state, n_jobs = n_jobs),
        param_distributions = params,
        random_state = random_state,
        scoring = "f1_macro",
        refit = True,
        cv = 5,
        n_jobs = n_jobs,
        return_train_score = True
    )
    
    model.fit(X_train, y_train)
    
    if verbose:
        summarize_crossvalidation(model)
        print("Training accuracy:", model.score(X_train, y_train))
        
        plot_learning_curve(model, list(params.keys()))
    
    return model.best_estimator_

def train_test_random_forest(
    X,
    y,
    test_size: float = 0.25,
    train_size: float | None = None,
    random_state: int = 10,
    n_jobs: int | None = None,
    verbose: bool = True
):
    X_train, X_test, y_train, y_test = train_test_split(
        X, y, test_size = test_size, train_size = train_size, random_state = random_state, stratify = y
    )
    
    if verbose:
        print("Training data dimensions:", X_train.shape)
        print("Testing data dimensions:", X_test.shape)
    
    model = train_random_forest(X_train = X_train, y_train = y_train, random_state = random_state, n_jobs =  n_jobs, verbose = verbose)
    accuracy = test_model(model = model, X_test = X_test, y_test = y_test, verbose = verbose)
    
    return TrainedModelWrapper(
        model = model,
        X = X,
        y = y,
        X_train = X_train,
        y_train = y_train,
        X_test = X_test,
        y_test = y_test,
        accuracy = accuracy
    )

ss_mcf7_pca_random_forest = train_test_random_forest(X_pca_ss_mcf7, y_pca_ss_mcf7, n_jobs = -1)

Training data dimensions: (187, 20)
Testing data dimensions: (63, 20)
========================= Training =========================
Best Parameters: {'bootstrap': True, 'max_depth': 5, 'max_features': 'sqrt', 'min_samples_leaf': 4, 'min_samples_split': 10, 'n_estimators': 25}
Best Score (CV avg): 0.9945945945945945
Number of decision trees: 25
Maximum tree depth: 5
Minimum samples to split: 10
Minimum samples per leaf: 4
Maximum features considered at each split: sqrt
Bootstrap: True
Feature importances: [4.87643283e-01 7.33065976e-02 6.00266784e-02 9.91685382e-02
 8.78119702e-02 8.30256017e-02 4.92950249e-03 6.29815380e-04
 5.22548266e-02 1.62137250e-02 8.48520910e-04 5.76192650e-04
 7.17375907e-03 1.37367232e-02 6.91512970e-04 1.09635964e-02
 2.10741782e-04 5.07314934e-04 2.81099111e-04 0.00000000e+00]
Training accuracy: 1.0

========================= Testing =========================
Confusion matrix:
             Predicted Hypo  Predicted Norm
Actual Hypo              31               0
Actual Norm               0              32
Accuracy: 1.0
Classification report:
               precision    recall  f1-score   support

        Hypo       1.00      1.00      1.00        31
        Norm       1.00      1.00      1.00        32

    accuracy                           1.00        63
   macro avg       1.00      1.00      1.00        63
weighted avg       1.00      1.00      1.00        63

ss_hcc_pca_random_forest = train_test_random_forest(X_pca_ss_hcc, y_pca_ss_hcc, n_jobs = -1)

Training data dimensions: (136, 34)
Testing data dimensions: (46, 34)
========================= Training =========================
Best Parameters: {'bootstrap': False, 'max_depth': 10, 'max_features': 'sqrt', 'min_samples_leaf': 10, 'min_samples_split': 2, 'n_estimators': 50}
Best Score (CV avg): 0.9852743878956579
Number of decision trees: 50
Maximum tree depth: 10
Minimum samples to split: 2
Minimum samples per leaf: 10
Maximum features considered at each split: sqrt
Bootstrap: False
Feature importances: [0.0064334  0.26821343 0.39898388 0.04399438 0.0053163  0.01452682
 0.01100844 0.01197652 0.01214636 0.02534943 0.0084228  0.02346837
 0.00617053 0.01136463 0.01097255 0.00409977 0.00640453 0.00222945
 0.01518255 0.00390206 0.00612587 0.00965581 0.00137775 0.00401086
 0.00990919 0.01525441 0.00095232 0.00268207 0.01150917 0.00975953
 0.00677455 0.00704284 0.01695411 0.0078253 ]
Training accuracy: 0.9926147162639153

========================= Testing =========================
Confusion matrix:
             Predicted Hypo  Predicted Norm
Actual Hypo              23               2
Actual Norm               1              20
Accuracy: 0.9347826086956522
Classification report:
               precision    recall  f1-score   support

        Hypo       0.96      0.92      0.94        25
        Norm       0.91      0.95      0.93        21

    accuracy                           0.93        46
   macro avg       0.93      0.94      0.93        46
weighted avg       0.94      0.93      0.93        46

ds_mcf7_pca_random_forest = train_test_random_forest(X_pca_ds_mcf7, y_pca_ds_mcf7, n_jobs = -1)

Training data dimensions: (16219, 761)
Testing data dimensions: (5407, 761)
========================= Training =========================
Best Parameters: {'n_estimators': 600, 'min_samples_split': 5, 'min_samples_leaf': 2, 'max_features': 'sqrt', 'max_depth': 10, 'class_weight': 'balanced', 'bootstrap': True}
Best Score (CV avg): 0.9246355565116522
Number of decision trees: 600
Maximum tree depth: 10
Minimum samples to split: 5
Minimum samples per leaf: 2
Maximum features considered at each split: sqrt
Bootstrap: True
Feature importances: [0.10531168 0.1298405  0.14376609 0.00890776 0.02425938 0.02095302
 0.002667   0.00859892 0.00154817 0.00104473 0.00099024 0.00243691
 0.00079045 0.00100409 0.00238867 0.00420052 0.00227854 0.00758007
 0.00386235 0.00065713 0.00139259 0.00101725 0.00311133 0.00083539
 0.00550909 0.00690945 0.00146923 0.00197005 0.00126192 0.00069935
 0.00125397 0.00175273 0.00311906 0.00106822 0.00351382 0.01149722
 0.01603425 0.00457864 0.000856   0.00063889 0.00058418 0.00055282
 0.00046926 0.0009799  0.00039346 0.00069561 0.00615267 0.00423996
 0.00028781 0.00071115 0.00033103 0.00030638 0.00031317 0.00055128
 0.00434507 0.00109667 0.00033435 0.00051367 0.00043008 0.00183359
 0.00028726 0.00035108 0.0004725  0.00035709 0.00052879 0.00059977
 0.00036044 0.00033627 0.00049262 0.00032868 0.0002728  0.00037134
 0.00028611 0.0002421  0.00067918 0.00034513 0.00031624 0.00036998
 0.00036177 0.0004618  0.00050901 0.00520248 0.00038603 0.00054757
 0.00092149 0.00077227 0.00048457 0.00033451 0.00029814 0.00049587
 0.00048314 0.00044835 0.00026905 0.00043941 0.0035963  0.00043558
 0.00365572 0.00072697 0.000371   0.00047457 0.00056056 0.00030292
 0.00054543 0.0004982  0.00063516 0.00039058 0.00041866 0.00034867
 0.00034766 0.00258043 0.00035767 0.00023369 0.00102244 0.0003174
 0.00036132 0.00185167 0.00068316 0.00114183 0.00100102 0.00026577
 0.00137913 0.00027339 0.0004236  0.00043554 0.00026215 0.00044223
 0.00055951 0.00046921 0.00024452 0.00068093 0.00025074 0.00041923
 0.00326383 0.00034942 0.00096892 0.00032075 0.00023898 0.00031992
 0.00033645 0.00150273 0.00028185 0.00498445 0.00044432 0.00198335
 0.00487231 0.00037248 0.00274954 0.00031893 0.00445734 0.00029965
 0.00489857 0.00033456 0.00027911 0.00021839 0.0004373  0.00026372
 0.00479623 0.00075912 0.00048689 0.00025701 0.00052364 0.00030578
 0.00061907 0.00032457 0.00040429 0.00057435 0.00302461 0.00041054
 0.00026883 0.00149999 0.00043445 0.00046755 0.00031501 0.00047634
 0.00042197 0.0006877  0.00033929 0.00041833 0.00048601 0.00044755
 0.00034759 0.00027137 0.00045445 0.00029416 0.00028925 0.00038233
 0.00046582 0.00024644 0.00030989 0.00027655 0.00123483 0.000325
 0.00037917 0.00031292 0.00024639 0.00034892 0.0002873  0.00034006
 0.00021666 0.00032756 0.0006794  0.00028754 0.00060304 0.00037378
 0.00028748 0.00043884 0.00033982 0.00031651 0.00027366 0.0002707
 0.00025703 0.00024373 0.00031308 0.00026148 0.00028144 0.00029017
 0.00039625 0.00024265 0.00029547 0.00032254 0.00031326 0.00040388
 0.00025408 0.00040568 0.00024113 0.0003202  0.00037481 0.00039226
 0.00027595 0.00036615 0.00035766 0.00145637 0.00032405 0.00037314
 0.00077107 0.00147386 0.00029295 0.00075031 0.00070221 0.00040906
 0.00073319 0.00092325 0.00036494 0.00039274 0.00025713 0.00043654
 0.00095813 0.00038295 0.00042241 0.00026091 0.0003436  0.00052236
 0.00037572 0.00038373 0.00030407 0.00029102 0.00030134 0.00042555
 0.00032727 0.00048261 0.0004383  0.000307   0.00022795 0.00083452
 0.00031865 0.00029358 0.0007342  0.00027078 0.0002773  0.00043468
 0.00123634 0.00019869 0.00035141 0.00100078 0.0006563  0.00043935
 0.00055915 0.00398463 0.00078137 0.000493   0.00060457 0.00047938
 0.0003374  0.00050419 0.00044541 0.0005015  0.00051325 0.00047102
 0.00031658 0.00083919 0.00044009 0.00041698 0.00031938 0.00127632
 0.00079488 0.00043122 0.00131825 0.00040851 0.00048043 0.00139255
 0.0019101  0.00027901 0.00165899 0.00041956 0.00047509 0.00078968
 0.00197831 0.00051353 0.00035162 0.00062458 0.00083784 0.00059051
 0.00071212 0.0035567  0.00031613 0.00063115 0.00450602 0.00317074
 0.00054999 0.00177576 0.00451337 0.0027391  0.0005018  0.00072116
 0.00030761 0.00345046 0.0011965  0.00227663 0.00029977 0.00043484
 0.0013455  0.00086135 0.00040533 0.00088912 0.00684443 0.00053045
 0.00072352 0.00209918 0.00051302 0.00042435 0.00038161 0.00067255
 0.00041105 0.00256786 0.00094846 0.0006198  0.00044308 0.00094528
 0.00051006 0.00064724 0.00043295 0.00118618 0.00051426 0.00029167
 0.00039577 0.00067263 0.00071725 0.00150253 0.000357   0.00024314
 0.00058133 0.0003918  0.00124965 0.00130424 0.00034879 0.00068637
 0.00142809 0.00066068 0.00046248 0.00057926 0.00100908 0.00081208
 0.00062834 0.00099082 0.00125176 0.00116258 0.00157917 0.00120138
 0.00178949 0.00209933 0.00294813 0.00061149 0.00185401 0.00039849
 0.00055386 0.00037537 0.00079814 0.00078185 0.00037376 0.00044869
 0.00079072 0.00038771 0.00065747 0.00128027 0.00100872 0.00040445
 0.00038032 0.00039798 0.00038978 0.00078005 0.00102374 0.00081737
 0.00239271 0.00070264 0.00073869 0.00034526 0.00056761 0.0005756
 0.00048465 0.00056605 0.000443   0.00084507 0.00053156 0.00053208
 0.00061853 0.00083416 0.0002911  0.00053235 0.00062375 0.00049954
 0.00112582 0.00250048 0.00044941 0.00141374 0.00048433 0.00113186
 0.00052561 0.00041922 0.00034755 0.00056605 0.00043394 0.00037361
 0.00045622 0.00067381 0.0004423  0.00053031 0.00041956 0.00107256
 0.00079628 0.00037066 0.00049364 0.00169165 0.00043079 0.00081818
 0.0003506  0.00050345 0.00111144 0.00041988 0.00044528 0.00103099
 0.00081886 0.00075629 0.00040685 0.00030028 0.000571   0.00036758
 0.00034553 0.00154734 0.00058072 0.00036249 0.00051047 0.00048956
 0.00070622 0.00099259 0.00049449 0.00045869 0.00029623 0.0005214
 0.00035259 0.00033357 0.00033984 0.00031595 0.00043209 0.00133602
 0.00133612 0.00037854 0.00083018 0.00083259 0.00038599 0.00076312
 0.00046143 0.00033456 0.00107701 0.00028666 0.00041183 0.00066119
 0.00054765 0.00071805 0.00049177 0.00063936 0.00030198 0.00037442
 0.00038605 0.00063903 0.000651   0.00083435 0.00032801 0.00097488
 0.00054724 0.00048431 0.00039327 0.00053298 0.00056161 0.00033175
 0.00027273 0.00053975 0.00043248 0.00055294 0.00027547 0.00031195
 0.00041462 0.00036052 0.00030161 0.00035572 0.00041122 0.0004305
 0.0004068  0.00040385 0.00038672 0.00069569 0.00027762 0.00049147
 0.00036931 0.00028512 0.00028854 0.00045523 0.0003621  0.00034837
 0.00023828 0.00022252 0.00033068 0.00043415 0.00034472 0.00033524
 0.00025828 0.000327   0.00037402 0.00029807 0.00029158 0.0003553
 0.00041216 0.00043521 0.00036297 0.00040371 0.0003658  0.00034593
 0.00034808 0.00061949 0.00031006 0.0003575  0.00042709 0.00039049
 0.00033915 0.00067957 0.00045901 0.00026427 0.00031127 0.00035066
 0.00031878 0.00032873 0.00026781 0.00036249 0.00043142 0.00085352
 0.00044547 0.0004821  0.00025455 0.00033042 0.00054598 0.00039337
 0.00032047 0.00034438 0.00035296 0.00026788 0.00037184 0.00030585
 0.00023866 0.00021862 0.00043793 0.00033364 0.00031334 0.00041238
 0.00030189 0.00070283 0.00031154 0.00035906 0.0003818  0.00037603
 0.00029103 0.00040264 0.00039551 0.00030778 0.00034299 0.00056394
 0.0003813  0.0003787  0.00027861 0.00032795 0.00030901 0.00033301
 0.00044004 0.00037484 0.00027191 0.00034013 0.00046865 0.00034942
 0.00082124 0.0003635  0.00038131 0.00056936 0.00038337 0.00042241
 0.00023855 0.0003306  0.00028391 0.00031094 0.00033818 0.00026622
 0.00022612 0.00031748 0.00035502 0.00034172 0.00038318 0.00038781
 0.00034458 0.00035536 0.00031096 0.00034362 0.00028379 0.00029752
 0.00031086 0.00042886 0.00027878 0.00050551 0.00032571 0.00046166
 0.0004208  0.00035131 0.00027304 0.00034648 0.00026529 0.0005698
 0.0003201  0.00028278 0.00030133 0.00034648 0.00030764 0.00026646
 0.00034035 0.00053556 0.00037732 0.00034907 0.00028583 0.00042038
 0.00025485 0.00025552 0.00031885 0.00031083 0.00032842 0.00027913
 0.00040893 0.00026345 0.00038301 0.00028446 0.00034416 0.00037919
 0.00056237 0.00037488 0.00036957 0.00046009 0.00037346 0.00046441
 0.0004007  0.00038125 0.00032505 0.00044276 0.00031539 0.00036569
 0.00033101 0.00030341 0.00075396 0.00035388 0.00037215 0.00034485
 0.00031414 0.00045129 0.00026111 0.00040362 0.00036975 0.00032967
 0.00031811 0.00041005 0.00026292 0.00026916 0.00039454 0.00028067
 0.00032729 0.00078951 0.0003763  0.00040922 0.00040862 0.00024828
 0.00028074 0.00033801 0.00028914 0.00039496 0.00033345 0.00035551
 0.00040217 0.00037222 0.00040346 0.00042141 0.00040244 0.00030947
 0.00047869 0.00052788 0.00060206 0.00036037 0.0004646  0.00046887
 0.00028266 0.00041449 0.000472   0.00032433 0.00038677 0.00039108
 0.00038871 0.00040167 0.00036875 0.00034833 0.00054566 0.00048124
 0.00038589 0.0003157  0.00035669 0.00034465 0.00032817 0.00035557
 0.00037518 0.00058563 0.00055149 0.00043627 0.00078367 0.00047058
 0.00040339 0.00036191 0.00030057 0.00049882 0.00030993 0.00052802
 0.0004069  0.00046966 0.00052695 0.00048346 0.00050339 0.00037464
 0.00032322 0.00060991 0.00059623 0.00044005 0.00047888]
Training accuracy: 0.9751030814126183

========================= Testing =========================
Confusion matrix:
             Predicted Hypo  Predicted Norm
Actual Hypo            2011             219
Actual Norm             136            3041
Accuracy: 0.9343443684113186
Classification report:
               precision    recall  f1-score   support

        Hypo       0.94      0.90      0.92      2230
        Norm       0.93      0.96      0.94      3177

    accuracy                           0.93      5407
   macro avg       0.93      0.93      0.93      5407
weighted avg       0.93      0.93      0.93      5407

ds_hcc_pca_random_forest = train_test_random_forest(X_pca_ds_hcc, y_pca_ds_hcc, n_jobs = -1)

Training data dimensions: (11011, 844)
Testing data dimensions: (3671, 844)
========================= Training =========================
Best Parameters: {'n_estimators': 400, 'min_samples_split': 5, 'min_samples_leaf': 5, 'max_features': 'sqrt', 'max_depth': 10, 'class_weight': 'balanced', 'bootstrap': True}
Best Score (CV avg): 0.8894451495995834
Number of decision trees: 400
Maximum tree depth: 10
Minimum samples to split: 5
Minimum samples per leaf: 5
Maximum features considered at each split: sqrt
Bootstrap: True
Feature importances: [0.01556211 0.01332938 0.02437381 0.00887438 0.05431453 0.15896834
 0.0013507  0.00367518 0.00127416 0.00183298 0.00448067 0.01776261
 0.00184872 0.00125025 0.00918616 0.00086553 0.00197497 0.01854113
 0.00168007 0.01304374 0.00213133 0.00061732 0.00244949 0.0016368
 0.00054001 0.00173133 0.00116409 0.00063809 0.00097648 0.00168106
 0.00133609 0.0007371  0.00084775 0.00210989 0.00224028 0.00602436
 0.00873917 0.00202338 0.01109634 0.00044959 0.00130168 0.00196231
 0.00074593 0.00373249 0.00143229 0.00463699 0.00272285 0.00119899
 0.00535349 0.00074939 0.00075856 0.00078748 0.00214877 0.00163909
 0.0007553  0.0012141  0.00060405 0.00046447 0.00061247 0.00294571
 0.00077433 0.00064645 0.00122328 0.00065023 0.0075631  0.00077905
 0.00080332 0.00223036 0.00135002 0.00212975 0.00111877 0.00119236
 0.0017247  0.00461049 0.00386357 0.00376939 0.00104736 0.00498201
 0.0010248  0.00330714 0.00079378 0.00048989 0.00059584 0.00049261
 0.00068026 0.00057738 0.00080852 0.00058372 0.00039259 0.00296434
 0.00058196 0.00059213 0.00051131 0.0008211  0.00067767 0.00061226
 0.00051419 0.00087139 0.00716098 0.00244524 0.00076278 0.00281701
 0.01749129 0.00084529 0.00055982 0.00222925 0.00048428 0.00041596
 0.0016672  0.00311293 0.00113994 0.0005407  0.0007626  0.00047769
 0.00052031 0.00050358 0.00053626 0.0010914  0.00065376 0.00053428
 0.00093926 0.00067285 0.00086375 0.00102802 0.00060578 0.00173141
 0.00684521 0.00070499 0.00050233 0.00054504 0.00052275 0.00045634
 0.00134417 0.00459004 0.00060823 0.00058205 0.001067   0.00062061
 0.00054693 0.00113608 0.00690824 0.00381467 0.00098319 0.00079215
 0.00415323 0.00094046 0.00166385 0.00116077 0.00096595 0.0008926
 0.00097181 0.00071386 0.00070076 0.00065067 0.00264815 0.00179797
 0.00061808 0.00046481 0.00061474 0.0005897  0.00205432 0.00043104
 0.00038956 0.00097699 0.00057125 0.00050097 0.00475183 0.00075185
 0.00159505 0.0013878  0.00072402 0.0032366  0.0010579  0.00186023
 0.00085914 0.00365674 0.00060932 0.00110362 0.0009037  0.00060107
 0.00109702 0.00201554 0.00088818 0.00177    0.00230438 0.00060168
 0.00437501 0.00094664 0.00180724 0.00139438 0.00090991 0.00189076
 0.00093883 0.00117496 0.00135589 0.00128304 0.01119274 0.00173013
 0.00108785 0.00066931 0.00387838 0.00148438 0.00069254 0.0006568
 0.00044375 0.00076008 0.00068554 0.00155672 0.00060751 0.00046874
 0.00056063 0.00167375 0.00065509 0.00072051 0.00066415 0.00072419
 0.00082454 0.00054486 0.00045343 0.00069155 0.00049277 0.00046386
 0.00030047 0.0004769  0.00053807 0.00061284 0.00045331 0.00050338
 0.00048455 0.00049045 0.00048583 0.00039619 0.00048068 0.00056404
 0.00045649 0.00049807 0.0004476  0.00066544 0.00078638 0.00131371
 0.00060537 0.00057138 0.0004336  0.0004147  0.00056396 0.00055417
 0.00051672 0.00052329 0.00047175 0.00203486 0.00089382 0.00060031
 0.00059987 0.00060181 0.00114545 0.00055001 0.00072426 0.00042265
 0.00236191 0.00075691 0.00040871 0.00185986 0.00088442 0.00043235
 0.00075746 0.00086747 0.0005696  0.00076044 0.00052714 0.00342677
 0.00067977 0.00054517 0.00103546 0.00082098 0.00051772 0.00063276
 0.00077017 0.00060814 0.00046105 0.00066417 0.00055127 0.00057197
 0.00148304 0.00065776 0.0004933  0.00039011 0.00060747 0.00052778
 0.00039206 0.00384451 0.00039337 0.00102246 0.0007918  0.00057602
 0.00032997 0.00106976 0.0004305  0.00042372 0.00058828 0.00067668
 0.00177687 0.00045052 0.00090498 0.00066602 0.00068188 0.00053053
 0.00064152 0.00031669 0.00054314 0.00035669 0.0008402  0.00044964
 0.00055899 0.00045394 0.00040204 0.00077508 0.00135424 0.00070711
 0.00042899 0.00073185 0.00044124 0.00041545 0.00070486 0.00056407
 0.00052535 0.00117377 0.00065302 0.00043444 0.000759   0.00041401
 0.00045835 0.00057571 0.00051076 0.00071106 0.00045357 0.0006442
 0.00055757 0.00053716 0.00042428 0.00045759 0.00049761 0.00053119
 0.0005204  0.00047922 0.00045042 0.00057014 0.00037927 0.00062553
 0.00047032 0.0004471  0.00050842 0.00040305 0.00059871 0.00041915
 0.00045843 0.00055262 0.00043224 0.00086933 0.00046888 0.00045559
 0.00046531 0.00042935 0.00042314 0.00052053 0.00037858 0.00036995
 0.00055344 0.00046626 0.00049141 0.00051588 0.00055633 0.00053725
 0.00047175 0.00043913 0.00093012 0.00039186 0.00054678 0.00048988
 0.0005382  0.00046831 0.00042162 0.00043037 0.00044103 0.00047019
 0.00050098 0.00054089 0.0004234  0.00043488 0.00046107 0.00060425
 0.00046603 0.00058242 0.00044285 0.00036235 0.00050451 0.00049373
 0.00037504 0.00040649 0.00045363 0.00042852 0.00038095 0.00037889
 0.00054208 0.00047065 0.00055819 0.00082124 0.00043254 0.00035073
 0.00041461 0.00041722 0.0005041  0.00040132 0.0005712  0.00055622
 0.00074771 0.00058211 0.00036429 0.00045066 0.00049865 0.00065056
 0.0005079  0.00049739 0.00062886 0.00047953 0.00037884 0.00044671
 0.00042038 0.00051152 0.00040804 0.00046951 0.00047351 0.00046245
 0.00043523 0.00053594 0.00037684 0.00052568 0.00054374 0.00056837
 0.00056615 0.00078979 0.00032377 0.00040263 0.00047492 0.00049456
 0.00042062 0.00050711 0.00045294 0.00053827 0.00058312 0.00052222
 0.0003461  0.00041605 0.00043773 0.00043503 0.00046172 0.00047388
 0.00036075 0.00045466 0.00043182 0.00046299 0.00053667 0.00048153
 0.0004796  0.00032893 0.00049793 0.00043586 0.00046089 0.00043909
 0.00047251 0.00037879 0.00039508 0.0006204  0.00047516 0.0004039
 0.00054548 0.00039939 0.00040089 0.00039647 0.00046816 0.00064493
 0.00043811 0.00042229 0.00047219 0.00032981 0.00042395 0.00040504
 0.00055575 0.00045233 0.00038079 0.00044759 0.00047355 0.00047244
 0.00038336 0.00042553 0.00033414 0.00043394 0.00063382 0.00044478
 0.00056546 0.00038854 0.00043654 0.00045097 0.00044877 0.0004271
 0.00044131 0.00031456 0.00046639 0.00038678 0.00055124 0.00028441
 0.00052403 0.00038867 0.00061439 0.00059023 0.00051487 0.00032175
 0.00058876 0.00031377 0.00065868 0.00034836 0.00037215 0.0005727
 0.00039767 0.00050772 0.00069996 0.00045412 0.00045022 0.00040029
 0.00042713 0.00061628 0.00036973 0.00043879 0.00036537 0.00030508
 0.00044016 0.00078747 0.00051579 0.00033836 0.00039105 0.00059108
 0.00054752 0.00041032 0.0005169  0.00069376 0.00063046 0.00051236
 0.00037725 0.00068524 0.00050268 0.00061998 0.00047021 0.00048317
 0.00052294 0.00053754 0.0004783  0.0005621  0.00046651 0.00048156
 0.0005337  0.00032022 0.00061965 0.00041965 0.0005098  0.0004917
 0.00049733 0.00055372 0.00057612 0.00052536 0.00037732 0.00059536
 0.00041714 0.00041961 0.0003624  0.00057209 0.00034381 0.00044579
 0.00051586 0.00039663 0.00071595 0.00052519 0.00055738 0.00042017
 0.00041026 0.00055984 0.00035082 0.00046102 0.00039487 0.00042902
 0.00062455 0.00054678 0.00045825 0.00037114 0.00040797 0.00044308
 0.00044061 0.00058309 0.00056002 0.00045243 0.00032297 0.00031919
 0.00033797 0.00038011 0.00037747 0.0003617  0.00057709 0.00039837
 0.00045427 0.00040537 0.00048965 0.00054909 0.00038214 0.00041778
 0.00043289 0.00042835 0.00049557 0.00051244 0.00046318 0.0004056
 0.00041031 0.00038141 0.00047444 0.00043974 0.00037476 0.00036134
 0.00032671 0.00054556 0.00039214 0.00050201 0.00045747 0.00041902
 0.00043253 0.00047241 0.0004119  0.00038622 0.00050233 0.00042848
 0.00043948 0.0004442  0.00039755 0.00048347 0.00048148 0.00031899
 0.00039389 0.00035602 0.00053936 0.00042077 0.00061889 0.000386
 0.00046729 0.00037702 0.00036526 0.00044787 0.00045822 0.00040826
 0.00049037 0.0004458  0.00042419 0.00037053 0.00054917 0.0003602
 0.00048142 0.0004795  0.00046837 0.0004291  0.00040798 0.00045988
 0.00037559 0.00038345 0.00037479 0.00044818 0.00045333 0.00039435
 0.00037874 0.00048985 0.0004038  0.00043736 0.00037463 0.0003931
 0.0004233  0.0005212  0.00043266 0.00038122 0.00035645 0.00036388
 0.00035413 0.00030773 0.00041858 0.00050964 0.00031148 0.0004099
 0.00048401 0.00043114 0.00048213 0.00046617 0.00045688 0.00040277
 0.00036055 0.00039382 0.00046754 0.00040982 0.00049309 0.00038646
 0.00033985 0.00045522 0.00055988 0.00035776 0.00044065 0.00029988
 0.00032753 0.00048493 0.000414   0.0003644  0.00033685 0.00040877
 0.00039934 0.00036964 0.00042756 0.00048326 0.00045033 0.00041154
 0.00041201 0.00053677 0.00041422 0.00036513 0.00047225 0.00041451
 0.00036835 0.00042879 0.00045731 0.00045082 0.00045776 0.0003839
 0.00035677 0.00049245 0.00041646 0.0004102  0.0004068  0.00042715
 0.00029613 0.00033614 0.00048362 0.00040997 0.00044753 0.00035816
 0.00042631 0.00041219 0.00049932 0.00036409 0.00034608 0.00044017
 0.00037566 0.00044554 0.00050801 0.00044757 0.0004577  0.00035813
 0.00037306 0.0005499  0.00042208 0.00040454 0.00053057 0.00028407
 0.00030356 0.00038941 0.00032431 0.00035801 0.00037545 0.00046236
 0.00055698 0.00045635 0.00041683 0.00028438 0.00031264 0.00046115
 0.00046821 0.00041125 0.00044694 0.00033223 0.00047251 0.00047906
 0.00050281 0.00042821 0.00041932 0.00042486 0.00042007 0.00040669
 0.00031386 0.00044459 0.00043993 0.00047744 0.00050077 0.00046564
 0.00046778 0.00044584 0.00035969 0.00043309 0.00044199 0.00040411
 0.00039904 0.00052408 0.00035861 0.00041926 0.00043548 0.0004615
 0.00039658 0.00043296 0.00033442 0.00046143 0.00050813 0.00039816
 0.00044554 0.00030297 0.00039987 0.0003612  0.00045544 0.0003402
 0.00048385 0.00038792 0.00045056 0.00039033 0.00047129 0.00046633
 0.00047721 0.00038866 0.00047778 0.00040715 0.00047695 0.00038039
 0.0004288  0.00041936 0.00030967 0.00035194 0.00039722 0.000382
 0.00032951 0.00040688 0.00034731 0.00047104 0.00053974 0.0003595
 0.00034621 0.0004059  0.00039647 0.00045877]
Training accuracy: 0.9760275073971344

========================= Testing =========================
Confusion matrix:
             Predicted Hypo  Predicted Norm
Actual Hypo            2045             180
Actual Norm             202            1244
Accuracy: 0.8959411604467448
Classification report:
               precision    recall  f1-score   support

        Hypo       0.91      0.92      0.91      2225
        Norm       0.87      0.86      0.87      1446

    accuracy                           0.90      3671
   macro avg       0.89      0.89      0.89      3671
weighted avg       0.90      0.90      0.90      3671

def train_mlp(
    X_train,
    y_train,
    random_state: int | None = None,
    n_jobs: int | None = None,
    verbose: bool = True
):
    if verbose:
        print("========================= Training =========================")
    
    params = {
        "hidden_layer_sizes": [(200,), (100, 50), (100, 100), (200, 100, 50)],
        "alpha": [1e-4, 1e-3, 1e-2, 1e-1],  # L2 regularization strength
    }
    
    model = GridSearchCV(
        estimator = MLPClassifier(
            max_iter = 500,
            random_state = random_state,
            early_stopping = True,
            n_iter_no_change = 10,
            verbose = False,
        ),
        param_grid = params,
        refit = True,
        cv = 5,
        n_jobs = n_jobs,
        return_train_score = True
    )
    
    model.fit(X_train, y_train)

    if verbose:
        summarize_crossvalidation(model)
        print("Training accuracy:", model.score(X_train, y_train))
        
        plot_learning_curve(model, list(params.keys()), log_scale_params=["alpha"])
    
    return model.best_estimator_

def train_test_mlp(
    X,
    y,
    test_size: float = 0.25,
    train_size: float | None = None,
    random_state: int = 10,
    n_jobs: int | None = None,
    verbose: bool = True
):
    X_train, X_test, y_train, y_test = train_test_split(
        X, y, test_size = test_size, train_size = train_size, random_state = random_state, stratify = y
    )
    
    if verbose:
        print("Training data dimensions:", X_train.shape)
        print("Testing data dimensions:", X_test.shape)
    
    model = train_mlp(X_train = X_train, y_train = y_train, random_state = random_state, n_jobs = n_jobs, verbose = verbose)
    accuracy = test_model(model = model, X_test = X_test, y_test = y_test, verbose = verbose)
    
    return TrainedModelWrapper(
        model = model,
        X = X,
        y = y,
        X_train = X_train,
        y_train = y_train,
        X_test = X_test,
        y_test = y_test,
        accuracy = accuracy
    )

ss_mcf7_pca_mlp = train_test_mlp(X_pca_ss_mcf7, y_pca_ss_mcf7, n_jobs = -1)

Training data dimensions: (187, 20)
Testing data dimensions: (63, 20)
========================= Training =========================
Best Parameters: {'alpha': 0.0001, 'hidden_layer_sizes': (100, 100)}
Best Score (CV avg): 0.9677098150782362
Max Iterations: 500
Number of iterations for convergence: 14
Training accuracy: 0.9893048128342246

========================= Testing =========================
Confusion matrix:
             Predicted Hypo  Predicted Norm
Actual Hypo              31               0
Actual Norm               0              32
Accuracy: 1.0
Classification report:
               precision    recall  f1-score   support

        Hypo       1.00      1.00      1.00        31
        Norm       1.00      1.00      1.00        32

    accuracy                           1.00        63
   macro avg       1.00      1.00      1.00        63
weighted avg       1.00      1.00      1.00        63

ss_hcc_pca_mlp = train_test_mlp(X_pca_ss_hcc, y_pca_ss_hcc, n_jobs = -1)

Training data dimensions: (136, 34)
Testing data dimensions: (46, 34)
========================= Training =========================
Best Parameters: {'alpha': 0.0001, 'hidden_layer_sizes': (100, 100)}
Best Score (CV avg): 0.8968253968253969
Max Iterations: 500
Number of iterations for convergence: 22
Training accuracy: 0.9705882352941176

========================= Testing =========================
Confusion matrix:
             Predicted Hypo  Predicted Norm
Actual Hypo              24               1
Actual Norm               1              20
Accuracy: 0.9565217391304348
Classification report:
               precision    recall  f1-score   support

        Hypo       0.96      0.96      0.96        25
        Norm       0.95      0.95      0.95        21

    accuracy                           0.96        46
   macro avg       0.96      0.96      0.96        46
weighted avg       0.96      0.96      0.96        46

ds_mcf7_pca_mlp = train_test_mlp(X_pca_ds_mcf7, y_pca_ds_mcf7, n_jobs = -1)

Training data dimensions: (16219, 761)
Testing data dimensions: (5407, 761)
========================= Training =========================
Best Parameters: {'alpha': 0.1, 'hidden_layer_sizes': (200,)}
Best Score (CV avg): 0.9808249428818134
Max Iterations: 500
Number of iterations for convergence: 13
Training accuracy: 0.990381651149886

========================= Testing =========================
Confusion matrix:
             Predicted Hypo  Predicted Norm
Actual Hypo            2157              73
Actual Norm              28            3149
Accuracy: 0.9813205104494174
Classification report:
               precision    recall  f1-score   support

        Hypo       0.99      0.97      0.98      2230
        Norm       0.98      0.99      0.98      3177

    accuracy                           0.98      5407
   macro avg       0.98      0.98      0.98      5407
weighted avg       0.98      0.98      0.98      5407

ds_hcc_pca_mlp = train_test_mlp(X_pca_ds_hcc, y_pca_ds_hcc, n_jobs = -1)

Training data dimensions: (11011, 844)
Testing data dimensions: (3671, 844)
========================= Training =========================
Best Parameters: {'alpha': 0.1, 'hidden_layer_sizes': (100, 100)}
Best Score (CV avg): 0.9560442102112428
Max Iterations: 500
Number of iterations for convergence: 39
Training accuracy: 0.9964580873671782

========================= Testing =========================
Confusion matrix:
             Predicted Hypo  Predicted Norm
Actual Hypo            2119             106
Actual Norm              61            1385
Accuracy: 0.954508308362844
Classification report:
               precision    recall  f1-score   support

        Hypo       0.97      0.95      0.96      2225
        Norm       0.93      0.96      0.94      1446

    accuracy                           0.95      3671
   macro avg       0.95      0.96      0.95      3671
weighted avg       0.96      0.95      0.95      3671

X_ss_mcf7 = ss_mcf7_norm.T.iloc[:]
y_ss_mcf7 = ["Hypo" if "hypo" in name.lower() else "Norm" for name in ss_mcf7_norm.columns]

X_ss_hcc = ss_hcc_norm.T.iloc[:]
y_ss_hcc = ["Hypo" if "hypo" in name.lower() else "Norm" for name in ss_hcc_norm.columns]

X_ds_mcf7 = ds_mcf7_norm.T.iloc[:]
y_ds_mcf7 = ["Hypo" if "hypo" in name.lower() else "Norm" for name in ds_mcf7_norm.columns]

X_ds_hcc = ds_hcc_norm.T.iloc[:]
y_ds_hcc = ["Hypo" if "hypo" in name.lower() else "Norm" for name in ds_hcc_norm.columns]

def get_selected_features(
    pipeline: Pipeline,
    X_train,
    step_names: list[str]
) -> list[str]:
    feature_names = X_train.columns
    
    for name in step_names:
        selector = pipeline.named_steps[name]
        mask = selector.get_support()
        feature_names = feature_names[mask]
        
    return feature_names.to_list()

def get_selected_pcs_from_model(estimator: BaseEstimator, verbose: bool = True):
    selector = SelectFromModel(estimator, prefit = True)
    mask = selector.get_support()
    pcs = [i + 1 for i in range(len(mask)) if mask[i]]
    
    if verbose:
        print(f"Top {len(pcs)} principal components:")
        print(pcs)
    
    return pcs

def count_and_sort_occurrences(feature_lists: list[list[str]], verbose: bool = True):
    top_features = []
    for feature_list in feature_lists:
        top_features += feature_list
    
    top_features = np.array(top_features)
    unique_features, feature_counts = np.unique(top_features, return_counts = True)
    
    top_features = np.asarray((unique_features, feature_counts)).T
    top_features = top_features[top_features[:, 1].argsort()][::-1]
    
    if verbose:
        print("Feature | Occurrences")
        print(top_features)
    
    return top_features

def filter_by_occurrences(feature_list: np.ndarray, n_occurrences: int):
    return [feature[0].item() for feature in feature_list if int(feature[1]) == n_occurrences]

def train_feature_selection_svm_rfe(
    X_train,
    y_train,
    estimator: BaseEstimator,
    estimator_params: dict[str, list],
    k: int | None = 1_000,
    random_state: int | None = None,
    n_jobs: int | None = None,
    verbose: bool = True
):
    """Train a feature selection pipeline using ANOVA, SVM, and RFE.

    Returns:
        tuple[Pipeline, list[str]]: Best pipeline and list of selected features.
    """
    if verbose:
        print("========================= Training =========================")
    
    n_samples = X_train.shape[0]
    params = {f"estimator__{param}": options for param, options in estimator_params.items()}
    
    if hasattr(estimator, "random_state") and random_state is not None:
        estimator.set_params(random_state = random_state)
        
    if hasattr(estimator, "n_jobs") and n_jobs is not None:
        estimator.set_params(n_jobs = n_jobs)
        
    univariate_selector = SelectKBest(k = k)
    svm_selector = SelectFromModel(LinearSVC(C = 0.025, random_state = random_state, max_iter = 10_000))
    rfe_selector = RFECV(estimator)
    
    pipeline = Pipeline([
        ("univariate", univariate_selector),
        ("svm", svm_selector),
        ("rfe", rfe_selector),
        ("estimator", estimator)
    ])
    
    pipeline = GridSearchCV(
        estimator = pipeline,
        param_grid = params,
        refit = True,
        scoring = "f1_macro",
        cv = 5,
        n_jobs = n_jobs,
        return_train_score = True
    ) if n_samples < 10_000 else RandomizedSearchCV(
        estimator = pipeline,
        param_distributions = params,
        random_state = random_state,
        refit = True,
        scoring = "f1_macro",
        cv = 5,
        n_jobs = n_jobs,
        return_train_score = True,
        
    )
    pipeline.fit(X_train, y_train)
    summarize_crossvalidation(pipeline)
    
    print("Training accuracy:", pipeline.score(X_train, y_train))
    
    plot_learning_curve(pipeline, list(params.keys()))
    
    best_pipeline: Pipeline = pipeline.best_estimator_
    selected_features = get_selected_features(best_pipeline, X_train, ["univariate", "svm", "rfe"])
    
    if verbose:
        print("Number of selected genes:", len(selected_features))
        print("Selected genes:", selected_features)
    
    return best_pipeline, selected_features

def feature_selection_svm_rfe(
    X,
    y,
    estimator: BaseEstimator,
    estimator_params: dict[str, list],
    test_size: float = 0.25,
    train_size: float | None = None,
    random_state: int | None = 10,
    n_jobs: int | None = None,
    verbose: bool = True
):
    """Train and test a feature selection pipeline using ANOVA, SVM, and RFE.

    Returns:
        tuple[Pipeline, list[str], float]: Best pipeline, list of selected features, test accuracy.
    """
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = test_size, train_size = train_size, random_state = random_state, stratify = y)
    
    if verbose:
        print("Training data dimensions:", X_train.shape)
        print("Testing data dimensions:", X_test.shape)
    
    pipeline, selected_features = train_feature_selection_svm_rfe(
        X_train = X_train,
        y_train = y_train,
        estimator = estimator,
        estimator_params = estimator_params,
        random_state = random_state,
        n_jobs = n_jobs,
        verbose = verbose
    )
    
    accuracy = test_model(pipeline, X_test, y_test, verbose)
    
    return pipeline, selected_features, accuracy

def train_feature_selection_random_forest(
    X_train,
    y_train,
    estimator: BaseEstimator,
    estimator_params: dict[str, list],
    k: int | None = 500,
    random_state: int | None = None,
    n_jobs: int | None = None,
    verbose: bool = True
):
    """Train a feature selection pipeline using ANOVA and random forest.

    Returns:
        tuple[Pipeline, list[str]]: Best pipeline and list of selected features.
    """
    if verbose:
        print("========================= Training =========================")
    
    n_samples = X_train.shape[0]
    params = {f"estimator__{param}": options for param, options in estimator_params.items()}
    
    if hasattr(estimator, "random_state") and random_state is not None:
        estimator.set_params(random_state = random_state)
        
    if hasattr(estimator, "n_jobs") and n_jobs is not None:
        estimator.set_params(n_jobs = n_jobs)
        
    univariate_selector = SelectKBest(k = k)
    random_forest_selector = SelectFromModel(RandomForestClassifier())
    
    pipeline = Pipeline([
        ("univariate", univariate_selector),
        ("random_forest", random_forest_selector),
        ("estimator", estimator)
    ])
    
    pipeline = GridSearchCV(
        estimator = pipeline,
        param_grid = params,
        refit = True,
        scoring = "f1_macro",
        cv = 5,
        n_jobs = n_jobs,
        return_train_score = True
    ) if n_samples < 10_000 else RandomizedSearchCV(
        estimator = pipeline,
        param_distributions = params,
        random_state = random_state,
        refit = True,
        scoring = "f1_macro",
        cv = 5,
        n_jobs = n_jobs,
        return_train_score = True,
    )
    pipeline.fit(X_train, y_train)
    summarize_crossvalidation(pipeline)
    
    print("Training accuracy:", pipeline.score(X_train, y_train))
    
    plot_learning_curve(pipeline, list(params.keys()))
    
    best_pipeline: Pipeline = pipeline.best_estimator_
    selected_features = get_selected_features(best_pipeline, X_train, ["univariate", "random_forest"])
    
    if verbose:
        print("Number of selected genes:", len(selected_features))
        print("Selected genes:", selected_features)
    
    return best_pipeline, selected_features

def feature_selection_random_forest(
    X,
    y,
    estimator: BaseEstimator,
    estimator_params: dict[str, list],
    test_size: float = 0.25,
    train_size: float | None = None,
    random_state: int | None = 10,
    n_jobs: int | None = None,
    verbose: bool = True
):
    """Train and test a feature selection pipeline using ANOVA, SVM, and RFE.

    Returns:
        tuple[Pipeline, list[str], float]: Best pipeline, list of selected features, test accuracy.
    """
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = test_size, train_size = train_size, random_state = random_state, stratify = y)
    
    if verbose:
        print("Training data dimensions:", X_train.shape)
        print("Testing data dimensions:", X_test.shape)
    
    pipeline, selected_features = train_feature_selection_random_forest(
        X_train = X_train,
        y_train = y_train,
        estimator = estimator,
        estimator_params = estimator_params,
        random_state = random_state,
        n_jobs = n_jobs,
        verbose = verbose
    )
    
    accuracy = test_model(pipeline, X_test, y_test, verbose)
    
    return pipeline, selected_features, accuracy

ss_mcf7_logit, ss_mcf7_logit_features, ss_mcf7_logit_accuracy = feature_selection_svm_rfe(
    X = X_ss_mcf7,
    y = y_ss_mcf7,
    estimator = LogisticRegression(max_iter = 10_000),
    estimator_params = {"C": [0.1, 1, 2]},
    n_jobs = -1
)

Training data dimensions: (187, 3000)
Testing data dimensions: (63, 3000)
========================= Training =========================

Best Parameters: {'estimator__C': 1}
Best Score (CV avg): 0.9893277893277894
Training accuracy: 1.0

Number of selected genes: 24
Selected genes: ['CYP1B1', 'DDIT4', 'TUBA1B', 'GFRA1', 'MT-CYB', 'SLC9A3R1', 'XBP1', 'MT-CO3', 'EMP2', 'MT-CO2', 'SLC39A6', 'PGK1', 'LDHA', 'STARD10', 'MT-CO1', 'SCD', 'FLNA', 'MT-ATP6', 'DHCR7', 'SULF2', 'GATA3', 'DDX5', 'NME1-NME2', 'ALDOA']
========================= Testing =========================
Confusion matrix:
             Predicted Hypo  Predicted Norm
Actual Hypo              31               0
Actual Norm               0              32
Accuracy: 1.0
Classification report:
               precision    recall  f1-score   support

        Hypo       1.00      1.00      1.00        31
        Norm       1.00      1.00      1.00        32

    accuracy                           1.00        63
   macro avg       1.00      1.00      1.00        63
weighted avg       1.00      1.00      1.00        63

ss_hcc_logit, ss_hcc_logit_features, ss_hcc_logit_accuracy = feature_selection_svm_rfe(
    X = X_ss_hcc,
    y = y_ss_hcc,
    estimator = LogisticRegression(max_iter = 10_000),
    estimator_params = {"C": [0.1, 1, 2]},
    n_jobs = -1
)

Training data dimensions: (136, 3000)
Testing data dimensions: (46, 3000)
========================= Training =========================

Best Parameters: {'estimator__C': 0.1}
Best Score (CV avg): 0.9775925925925926
Training accuracy: 1.0

Number of selected genes: 166
Selected genes: ['DDIT4', 'ANGPTL4', 'CCNB1', 'IGFBP3', 'AKR1C2', 'NDRG1', 'KRT4', 'FN1', 'MMP1', 'SPP1', 'EGLN3', 'CA9', 'CDC20', 'AURKA', 'PLIN2', 'UPK1B', 'AKR1C1', 'FOS', 'LAMB3', 'LY6D', 'H4C3', 'AKR1C3', 'TPX2', 'PLAU', 'CXCL1', 'FAM83A', 'BNIP3', 'INSIG1', 'KRT19', 'BHLHE40', 'TXNIP', 'THBS1', 'ALDOC', 'ID3', 'CEACAM5', 'FTH1', 'GPRC5A', 'CCNB2', 'KPNA2', 'FTL', 'PLK2', 'DKK1', 'KCTD11', 'SLC2A1', 'CLDN4', 'KIF23', 'PGK1', 'SLC6A8', 'KIF2C', 'LOXL2', 'CHAC1', 'SPAG5', 'F3', 'WTAPP1', 'CSTB', 'HSPA5', 'DHCR7', 'HERPUD1', 'FGFBP1', 'CDKN1A', 'PFKFB3', 'DHRS3', 'LDHA', 'SLCO4A1', 'KDM5B', 'KRT8', 'PRC1', 'ADM', 'KNSTRN', 'FDFT1', 'CKS2', 'TMSB10', 'SLC38A2', 'CD44', 'FOSL2', 'JUP', 'KYNU', 'ALDH1A3', 'S100A2', 'KRT18', 'ZWINT', 'PRSS23', 'HBP1', 'SQSTM1', 'MYC', 'JUNB', 'H1-0', 'C10orf55', 'MSMO1', 'ERO1A', 'SRXN1', 'CKAP2', 'TFRC', 'SEMA4B', 'ITGA6', 'EIF5', 'P4HA1', 'TRIM29', 'SLC20A1', 'TRIM16', 'CDC6', 'IRF6', 'HMGCS1', 'GPX2', 'GPI', 'HSPA8', 'ISG15', 'ALDOA', 'CAV1', 'BIRC5', 'TXN', 'TUBB', 'PCDH1', 'TUBB4B', 'MT-CO3', 'ACAT2', 'POLR2A', 'IER2', 'AMOTL2', 'FSCN1', 'MT-CYB', 'BLCAP', 'PLOD2', 'TUBA1B', 'HES1', 'NQO1', 'DCBLD2', 'HSP90B1', 'FYB1', 'UGDH', 'LMNA', 'MRNIP', 'HRH1', 'PCNA', 'PRNP', 'BNIP3L', 'TPBG', 'C4orf3', 'MT-RNR1', 'EGLN1', 'PRDX1', 'UBB', 'HMGA1', 'PSMD2', 'NUP188', 'HSP90AA1', 'NRP1', 'SRM', 'HSPH1', 'BAG3', 'MIF-AS1', 'MIF', 'HLA-A', 'LDHB', 'CDK2AP2', 'PERP', 'EIF4A2', 'PPIF', 'FUT11', 'FAM162A', 'TYSND1', 'CLDN7', 'P4HA2', 'CANX', 'NOLC1', 'VCPIP1']
========================= Testing =========================
Confusion matrix:
             Predicted Hypo  Predicted Norm
Actual Hypo              24               1
Actual Norm               0              21
Accuracy: 0.9782608695652174
Classification report:
               precision    recall  f1-score   support

        Hypo       1.00      0.96      0.98        25
        Norm       0.95      1.00      0.98        21

    accuracy                           0.98        46
   macro avg       0.98      0.98      0.98        46
weighted avg       0.98      0.98      0.98        46

ds_mcf7_logit, ds_mcf7_logit_features, ds_mcf7_logit_accuracy = feature_selection_svm_rfe(
    X = X_ds_mcf7,
    y = y_ds_mcf7,
    estimator = LogisticRegression(max_iter = 10_000),
    estimator_params = {"C": [0.1, 1, 2]},
    n_jobs = -1
)

Training data dimensions: (16219, 3000)
Testing data dimensions: (5407, 3000)
========================= Training =========================
Best Parameters: {'estimator__C': 0.1}
Best Score (CV avg): 0.9732560929550514
Training accuracy: 0.9826839329314294

Number of selected genes: 377
Selected genes: ['MT-RNR2', 'TFF1', 'MT-RNR1', 'GDF15', 'MT-CO3', 'MT-ND4', 'MT-ND3', 'MT-CYB', 'IGFBP5', 'TMSB10', 'MT-ATP6', 'MT-CO2', 'MT-TS1', 'MT-ND6', 'MT-ND2', 'MTND1P23', 'MT-ND4L', 'MT-ND5', 'MT-TN', 'MT-ND1', 'MT-TA', 'MT-TQ', 'HES1', 'MT-TM', 'LGALS1', 'TMEM64', 'MTND2P28', 'MT-TE', 'H19', 'MT-ATP8', 'H2AC12', 'NCDN', 'MT-TY', 'TOB1', 'H2AC20', 'MT-TP', 'ANKRD52', 'MT-TD', 'C16orf91', 'ATN1', 'WSB2', 'GPM6A', 'ZFP36', 'VMP1', 'TFF3', 'KMT2D', 'FGF23', 'CRTC2', 'CSK', 'PLBD2', 'ITPK1', 'PLEC', 'GOLGA4', 'PLCD3', 'PTP4A2', 'TIAM1', 'SOX4', 'BTBD9', 'H2AC11', 'CBFA2T3', 'PROSER1', 'ARF3', 'PARD6B', 'RPL13', 'TPI1', 'BTN3A2', 'GREM1', 'RNF146', 'S100A10', 'CHAC2', 'ATXN2L', 'TGFB3', 'MGRN1', 'CAPZA1', 'FAM189B', 'GSE1', 'CERS2', 'ENO1', 'SLC48A1', 'PKIB', 'RHOD', 'BLOC1S3', 'KRT19', 'RPL34', 'TCF20', 'LINC01291', 'FAM102A', 'PRRG3', 'GABPB2', 'CAMK2N1', 'VPS9D1-AS1', 'TAF13', 'INCENP', 'ZNRF1', 'NINJ1', 'ZBTB34', 'DSP', 'ZNF480', 'CALHM2', 'MSMB', 'KCNJ2', 'ZBTB20', 'TPD52L1', 'HSPH1', 'HMGA1', 'CASP8AP2', 'ZNF302', 'ELOA', 'GPATCH4', 'SNX24', 'DVL3', 'SNX27', 'YTHDF3', 'GAB2', 'PACS1', 'NLK', 'THAP1', 'KCNJ3', 'LDLRAP1', 'TRAK2', 'CAMSAP2', 'PPM1G', 'NCALD', 'LRRFIP2', 'DNAJA1', 'SMKR1', 'MAPKAPK2', 'ZNF702P', 'NACC1', 'TRIM37', 'RFK', 'FBXL16', 'TCHP', 'ISCU', 'RABEP1', 'CACNG4', 'RPSAP48', 'WWC3', 'GDAP2', 'SRCAP', 'USP32', 'FLOT2', 'MAFF', 'NCOA1', 'TWNK', 'AKAP5', 'NEDD4L', 'APOOL', 'CCDC18', 'RAB27A', 'BRPF3', 'BCAS3', 'GATAD2A', 'NSD1', 'NPM1P40', 'ANKRD40', 'ILRUN', 'PSMD14', 'STRBP', 'TPM1', 'CAV1', 'MPHOSPH9', 'ANXA6', 'PRXL2C', 'CDC25B', 'KIF14', 'PYGO2', 'ZNF688', 'KHSRP', 'BAP1', 'MDM2', 'RAB5C', 'PAQR8', 'SOS1', 'KRT80', 'SECISBP2L', 'BOLA3', 'DNAJA4', 'THRB', 'ARPP19', 'S100A11', 'FRS2', 'RGPD4-AS1', 'BRIP1', 'PRR12', 'TEDC2-AS1', 'RPL15', 'DKC1', 'C9orf78', 'NBEAL2', 'SETD3', 'FEM1A', 'SLC25A24', 'ARMC6', 'SLC13A5', 'CFAP97', 'NEDD1', 'PHLDA2', 'MARK3', 'SPATS2L', 'PAPOLA', 'MT2A', 'ZNF354A', 'SET', 'ATXN1L', 'SCYL2', 'ZNF703', 'SRFBP1', 'UBA52', 'MGLL', 'LAD1', 'ZC3H15', 'SLC25A48', 'RAD23A', 'EIF4G2', 'HOXC13', 'PITPNA', 'TAF9B', 'LXN', 'SERINC5', 'FBRS', 'SMC5', 'RAI14', 'TRIM44', 'MYO5C', 'AKT1S1', 'TBKBP1', 'EIF2B4', 'PRR34-AS1', 'PSME4', 'PDAP1', 'ARHGAP26', 'ELP3', 'SENP6', 'DNAJC21', 'FAM104A', 'CS', 'ABL1', 'EIF3A', 'H2AX', 'MARK2', 'LCLAT1', 'S100P', 'RCC1L', 'ANKRD17', 'TMEM259', 'RAB1B', 'GAPDH', 'TMEM258', 'SSX2IP', 'PDS5A', 'FAM177A1', 'NAA10', 'CNOT9', 'PGK1', 'PKM', 'KLHL8', 'BCL3', 'PRMT6', 'CACNA1A', 'GOLGA3', 'SOCS2', 'PPP1R12B', 'DCTN1', 'C7orf50', 'ZMIZ1', 'PGAM5', 'RPL30', 'ARNTL2', 'PREX1', 'LYAR', 'PRRC2C', 'PCYT1A', 'GLE1', 'ZFC3H1', 'BMPR1B', 'RBBP6', 'ZNF764', 'RAB35', 'ENOX2', 'LMNB2', 'ZNF326', 'ARID1B', 'TIMELESS', 'PFDN4', 'LPP', 'SYNE2', 'ZRANB1', 'PLCB4', 'CBX3', 'NOL4L', 'SPRY1', 'RPS6KA6', 'CKS2', 'SMC6', 'AURKA', 'BICDL1', 'DBNDD1', 'CRNDE', 'C2orf49', 'TPM4', 'FAM111B', 'KPNA2', 'NCKAP1', 'INF2', 'CSNK2A2', 'FARP1', 'MAP3K13', 'NCOA5', 'DNAJA3', 'RRP1B', 'HCFC1', 'ACTB', 'GATA3', 'DHX38', 'TSPYL1', 'TBC1D9', 'IWS1', 'FAM50A', 'AFF1', 'WDR43', 'SHISA5', 'CLTB', 'ETF1', 'RSRC2', 'GNAQ', 'BAZ2A', 'TARS1', 'KCNQ1OT1', 'YWHAB', 'EBAG9', 'PITX1', 'KPNA4', 'UTP18', 'PSMD5', 'NFIC', 'PHF20L1', 'PATL1', 'POLB', 'TNIP2', 'ARIH1', 'KLC1', 'ZBTB7A', 'NPLOC4', 'ARFGEF1', 'TRAF3IP2', 'BMS1', 'INPP4B', 'MYH14', 'KITLG', 'ATF5', 'TBCA', 'PICALM', 'FAM13B', 'FBXL18', 'MYO10', 'TAOK3', 'BBOF1', 'CLSPN', 'PAK2', 'STRIP1', 'IFI27L2', 'LTBR', 'ESRP2', 'C6orf62', 'AAMP', 'PMEPA1', 'UBE2Q2', 'DHX37', 'SLAIN2', 'OTUD7B', 'RPL23', 'NCBP3', 'ATRX', 'CCM2', 'NOM1', 'SMIM27']
========================= Testing =========================
Confusion matrix:
             Predicted Hypo  Predicted Norm
Actual Hypo            2141              89
Actual Norm              54            3123
Accuracy: 0.9735528019234326
Classification report:
               precision    recall  f1-score   support

        Hypo       0.98      0.96      0.97      2230
        Norm       0.97      0.98      0.98      3177

    accuracy                           0.97      5407
   macro avg       0.97      0.97      0.97      5407
weighted avg       0.97      0.97      0.97      5407

ds_hcc_logit, ds_hcc_logit_features, ds_hcc_logit_accuracy = feature_selection_svm_rfe(
    X = X_ds_hcc,
    y = y_ds_hcc,
    estimator = LogisticRegression(max_iter = 10_000),
    estimator_params = {"C": [0.1, 1, 2]},
    n_jobs = -1
)

Training data dimensions: (11011, 3000)
Testing data dimensions: (3671, 3000)
========================= Training =========================

Best Parameters: {'estimator__C': 0.1}
Best Score (CV avg): 0.9421202743207646
Training accuracy: 0.9611172478461597

Number of selected genes: 399
Selected genes: ['BCYRN1', 'IGFBP3', 'H2AC11', 'RAPGEF3', 'DDIT4', 'MB', 'MT-TV', 'ADAP1', 'MT-TL1', 'NDRG1', 'MIR210HG', 'MT-TQ', 'SPN', 'ZNF263', 'MT-CO3', 'CACHD1', 'BTBD9', 'GDPGP1', 'ARTN', 'LINC01304', 'CACNB2', 'HELQ', 'NAXD', 'GPM6A', 'MT-TS1', 'MT-TA', 'CRIP2', 'NEAT1', 'DUSP9', 'PRR5L', 'USP35', 'MT-ND1', 'MT-CYB', 'H19', 'H2AC12', 'CNR2', 'FGF23', 'DANT1', 'AKR1C2', 'TMSB10', 'EHBP1L1', 'LDHA', 'MT-ND6', 'EFNA2', 'CITED2', 'H4C5', 'CNOT6L', 'CLDN4', 'CPNE2', 'DTNB', 'GABRE', 'LINC02511', 'MT-ATP6', 'LGALS1', 'NUPR2', 'H2AC16', 'MPDU1', 'ATXN2L', 'PGAM1', 'PPIL1', 'NCL', 'COL6A3', 'ABO', 'RPL17', 'SLC2A1', 'C4orf3', 'NOP10', 'KLC2', 'MT-CO2', 'NCALD', 'EGLN3', 'OPTN', 'ANKRD9', 'TRAK1', 'CNNM2', 'RRAS', 'BNIP3', 'ENTR1', 'FGF8', 'B4GALT1', 'GPI', 'LIMCH1', 'MIR663AHG', 'SREK1IP1P1', 'FBXL17', 'LINC02541', 'P4HA1', 'H2BC4', 'RPL41', 'COX8A', 'MT-TS2', 'PROSER1', 'PGK1', 'CAMK2N1', 'HEPACAM', 'MSR1', 'GDI1', 'SIGMAR1', 'AHNAK2', 'GCAT', 'SINHCAFP3', 'CTXN1', 'LINC01133', 'POLR3GL', 'HES4', 'PDCD4', 'TNFRSF12A', 'ENKD1', 'SHOX', 'RGPD4-AS1', 'HIF3A', 'S100A10', 'APOOL', 'RTL8C', 'ARNTL', 'HMGB2', 'NEDD9', 'TMEM70', 'FASTKD5', 'DAAM1', 'HSP90AB1', 'ZBED2', 'EFNA5', 'PSMG1', 'TMSB4XP4', 'NPM1P40', 'RPL39', 'AJAP1', 'SAMD4A', 'WDR77', 'PAQR7', 'NDUFB4', 'BTN3A2', 'VIT', 'ARHGDIA', 'H3C2', 'FOSL2', 'MIXL1', 'MCM3AP', 'GJB3', 'PRRC2A', 'FSD1L', 'IVL', 'KCNJ3', 'BNIP3L', 'S100A11', 'BMPR1B', 'H2BC9', 'TNNT1', 'CEP120', 'LINC02367', 'RAB30', 'ZBED4', 'RAB11FIP4', 'RNF122', 'NEDD4L', 'RAB2B', 'RPS27', 'CSTB', 'C1orf53', 'NCK1', 'CPEB1', 'MLLT3', 'MELTF-AS1', 'TCF7L1', 'NT5C', 'MT1E', 'RPSAP48', 'TNFSF13B', 'ECH1', 'NDUFA8', 'MIOS-DT', 'KRT19', 'ZNF318', 'POLDIP2', 'VPS45', 'ZNF418', 'YTHDF3', 'MT-ND4L', 'PI4KB', 'ADARB1', 'AXL', 'CACNA1A', 'TUBB6', 'NRG4', 'NMD3', 'FAM126B', 'PHACTR1', 'TXNRD2', 'BAP1', 'HSPD1', 'PLD1', 'JAKMIP3', 'DDX23', 'RPL28', 'ANKEF1', 'RPS6KA6', 'DUSP5', 'SH3RF1', 'ARHGEF26', 'SLC6A8', 'JUN', 'OVOL1', 'APEH', 'CAVIN3', 'ZNF302', 'DCAKD', 'ARL2', 'LINC01902', 'RBSN', 'CREB1', 'TATDN2', 'PRRG3', 'RPS21', 'ALDOC', 'MMP2', 'POLE4', 'PTGR1', 'CCDC168', 'GBP1P1', 'TSHZ2', 'IRF2BPL', 'ADM', 'CAST', 'RPS29', 'AKR1C1', 'PCDHGA10', 'RGS10', 'TGDS', 'EPHX1', 'KAT7', 'NEUROD2', 'CFAP251', 'MXRA5', 'PFKFB3', 'PLOD2', 'PPTC7', 'ING2', 'CD47', 'ZNF33B', 'KIRREL1', 'KDM3A', 'UQCC2', 'FUT11', 'MXI1', 'MED18', 'SYNJ2', 'SNHG18', 'RNF25', 'AKT1S1', 'KLLN', 'NCAM1', 'RAB12', 'PDLIM1', 'MT1X', 'DERA', 'YTHDF1', 'AMFR', 'CEP83', 'SF3B4', 'POLR3A', 'PHRF1', 'GYS1', 'SRA1', 'EPPK1', 'SYT14', 'FAM162A', 'KCNJ2', 'ARMC6', 'MKNK1', 'HSP90AA1', 'INHBA', 'FYN', 'BTBD7P1', 'CENPB', 'RHBDD2', 'SNX22', 'SLC2A6', 'LINC01116', 'ISOC2', 'MPHOSPH6', 'JUND', 'RAB3GAP1', 'MNS1', 'DTYMK', 'TOLLIP', 'GIN1', 'FAH', 'GOLGA4', 'TMEM256', 'DGKD', 'WDR43', 'CAMSAP2', 'NACA4P', 'ARHGAP42', 'NDUFC1', 'GAPDH', 'TMEM238', 'GRK2', 'DNAH11', 'ZBTB2', 'TRIM44', 'CIAO2A', 'UTP3', 'CALM2', 'BRMS1', 'PCDHB1', 'TTL', 'FOSL1', 'YKT6', 'ACSL4', 'CCDC34', 'SAT2', 'RHOT2', 'MAD2L1', 'DBT', 'RPL27A', 'RPL37A', 'NUP93', 'AMOTL2', 'PPP4R2', 'CARM1', 'VEGFB', 'NCLN', 'MLLT6', 'MAP2K3', 'DNAAF5', 'PSMA7', 'DDX54', 'TCEAL9', 'RPLP0P2', 'KRT4', 'SNORD3B-1', 'FEM1A', 'TRIM52-AS1', 'MCM4', 'CCNG2', 'YWHAZ', 'ARID5B', 'MRPL55', 'KMT2D', 'SPG21', 'ZC3H15', 'EMP2', 'LETM1', 'EIF3J', 'SNRNP70', 'RHOD', 'MAFF', 'MAZ', 'UQCR11', 'PLCE1', 'CPTP', 'ARHGEF7', 'STMN1', 'ZNF202', 'SNHG9', 'HMGA1', 'CLIC1', 'ZHX1', 'TSPO', 'TPD52L1', 'FRY', 'DNMT3A', 'ARL13B', 'SMARCB1', 'RRS1', 'HEY1', 'SLC25A48', 'TMEM80', 'DYSF', 'MTA2', 'C19orf53', 'ARSA', 'DGKZ', 'VRK3', 'UIMC1', 'PSIP1', 'ZNF688', 'CMIP', 'PPIG', 'EXOC7', 'TAF15', 'MARCKS', 'AK4', 'KIF5B', 'ATP5F1E', 'IRAK1', 'BRAT1', 'TSR1', 'SART1', 'CAP1', 'SETD2', 'METTL26', 'STC2', 'DDIT3', 'KEAP1', 'DLD', 'CLIP2']
========================= Testing =========================
Confusion matrix:
             Predicted Hypo  Predicted Norm
Actual Hypo            2115             110
Actual Norm              94            1352
Accuracy: 0.944429310814492
Classification report:
               precision    recall  f1-score   support

        Hypo       0.96      0.95      0.95      2225
        Norm       0.92      0.93      0.93      1446

    accuracy                           0.94      3671
   macro avg       0.94      0.94      0.94      3671
weighted avg       0.94      0.94      0.94      3671

print("SmartSeq MCF7 Logistic Regression")
ss_mcf7_pca_logit_pcs = get_selected_pcs_from_model(ss_mcf7_pca_logit.model)
print()

print("SmartSeq HCC Logistic Regression")
ss_hcc_pca_logit_pcs = get_selected_pcs_from_model(ss_hcc_pca_logit.model)
print()

print("DropSeq MCF7 Logistic Regression")
ds_mcf7_pca_logit_pcs = get_selected_pcs_from_model(ds_mcf7_pca_logit.model)
print()

print("DropSeq HCC Logistic Regression")
ds_hcc_pca_logit_pcs = get_selected_pcs_from_model(ds_hcc_pca_logit.model)
print()

SmartSeq MCF7 Logistic Regression
Top 9 principal components:
[1, 3, 6, 8, 12, 15, 16, 17, 18]

SmartSeq HCC Logistic Regression
Top 10 principal components:
[2, 3, 9, 10, 12, 13, 16, 17, 23, 26]

DropSeq MCF7 Logistic Regression
Top 310 principal components:
[1, 2, 3, 5, 6, 8, 13, 14, 15, 16, 17, 18, 19, 20, 25, 26, 27, 28, 29, 30, 31, 32, 33, 36, 37, 40, 43, 44, 45, 46, 52, 54, 55, 56, 57, 59, 60, 61, 62, 65, 66, 69, 71, 74, 81, 82, 85, 87, 88, 91, 92, 94, 95, 96, 99, 100, 104, 105, 107, 110, 112, 114, 115, 116, 118, 119, 120, 121, 127, 128, 133, 135, 138, 140, 141, 142, 145, 146, 147, 149, 153, 157, 160, 161, 167, 170, 172, 173, 175, 177, 182, 186, 188, 190, 191, 193, 195, 197, 198, 200, 201, 203, 204, 205, 206, 211, 212, 213, 218, 219, 221, 230, 231, 232, 234, 235, 236, 239, 240, 241, 247, 249, 252, 253, 254, 258, 263, 264, 265, 267, 269, 271, 273, 275, 279, 281, 282, 286, 287, 291, 293, 302, 305, 312, 317, 318, 319, 322, 323, 327, 332, 337, 339, 341, 342, 344, 348, 350, 352, 353, 361, 364, 365, 370, 371, 375, 376, 377, 380, 381, 383, 385, 387, 389, 391, 392, 393, 398, 399, 400, 401, 402, 403, 406, 408, 409, 411, 415, 418, 419, 420, 426, 427, 429, 430, 431, 433, 434, 435, 436, 437, 438, 442, 446, 449, 455, 459, 460, 461, 462, 464, 466, 467, 469, 470, 471, 475, 481, 484, 485, 486, 487, 491, 494, 495, 496, 497, 499, 504, 506, 507, 508, 510, 512, 514, 517, 520, 522, 527, 534, 538, 540, 541, 543, 546, 552, 555, 556, 557, 564, 565, 576, 577, 580, 582, 585, 591, 592, 596, 597, 598, 599, 602, 606, 610, 612, 615, 621, 623, 624, 626, 631, 632, 633, 642, 646, 647, 650, 652, 653, 655, 658, 661, 666, 672, 674, 675, 677, 681, 682, 685, 696, 698, 700, 702, 718, 722, 724, 726, 732, 733, 734, 741, 742, 746, 751, 754, 755, 756, 758]

DropSeq HCC Logistic Regression
Top 320 principal components:
[2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 15, 16, 18, 19, 20, 21, 23, 24, 26, 27, 29, 30, 31, 32, 34, 36, 37, 38, 39, 41, 45, 46, 47, 48, 49, 53, 54, 55, 60, 63, 65, 69, 72, 76, 77, 80, 86, 88, 89, 90, 92, 94, 95, 96, 97, 99, 102, 103, 106, 115, 117, 118, 120, 121, 123, 124, 125, 126, 127, 131, 135, 136, 137, 139, 140, 141, 142, 143, 145, 147, 148, 151, 152, 153, 154, 155, 157, 159, 161, 162, 166, 167, 169, 170, 171, 172, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 187, 189, 190, 191, 193, 197, 198, 199, 200, 201, 205, 207, 208, 210, 213, 215, 217, 218, 219, 220, 221, 224, 225, 227, 229, 230, 231, 233, 234, 235, 237, 238, 239, 240, 243, 245, 247, 249, 254, 255, 257, 259, 260, 261, 262, 263, 265, 266, 267, 270, 272, 275, 282, 290, 292, 294, 295, 297, 300, 301, 303, 308, 310, 312, 313, 315, 318, 319, 325, 326, 329, 332, 334, 336, 339, 341, 344, 345, 350, 351, 356, 357, 358, 359, 360, 362, 369, 371, 372, 373, 374, 375, 377, 379, 380, 383, 384, 391, 396, 399, 401, 403, 408, 409, 412, 413, 414, 415, 420, 423, 427, 438, 439, 444, 445, 447, 448, 450, 451, 453, 457, 461, 466, 474, 479, 490, 494, 499, 503, 507, 509, 515, 516, 521, 525, 528, 540, 550, 551, 561, 562, 563, 564, 566, 567, 575, 576, 577, 581, 582, 583, 584, 586, 594, 598, 600, 603, 604, 609, 610, 612, 613, 622, 630, 637, 640, 641, 642, 651, 653, 657, 672, 691, 692, 693, 698, 700, 701, 713, 717, 718, 730, 746, 747, 749, 756, 758, 760, 761, 763, 766, 769, 777, 781, 783, 784, 785, 789, 792, 793, 794, 799, 807, 808, 809, 810, 813, 814, 831, 835, 841, 842, 843]

ss_mcf7_svm, ss_mcf7_svm_features, ss_mcf7_svm_accuracy = feature_selection_svm_rfe(
    X = X_ss_mcf7,
    y = y_ss_mcf7,
    estimator = LinearSVC(max_iter = 10_000),
    estimator_params = {"C": [0.025, 0.1, 1, 5]},
    n_jobs = -1
)

Training data dimensions: (187, 3000)
Testing data dimensions: (63, 3000)
========================= Training =========================

Best Parameters: {'estimator__C': 0.025}
Best Score (CV avg): 1.0
Training accuracy: 1.0

Number of selected genes: 33
Selected genes: ['DDIT4', 'NR4A1', 'FOS', 'STC2', 'HILPDA', 'MCM7', 'MT-CYB', 'TMEM64', 'XBP1', 'CRABP2', 'MT-CO3', 'EMP2', 'MT-CO2', 'PGK1', 'LDHA', 'STARD10', 'MT-CO1', 'DYNC2I2', 'FLNA', 'TMSB10', 'IFITM3', 'DSP', 'FAM162A', 'SULF2', 'QSOX1', 'ARPC1B', 'SYTL2', 'PSAP', 'CD9', 'HNRNPA2B1', 'GATA3', 'ATP9A', 'NME1-NME2']
========================= Testing =========================
Confusion matrix:
             Predicted Hypo  Predicted Norm
Actual Hypo              31               0
Actual Norm               0              32
Accuracy: 1.0
Classification report:
               precision    recall  f1-score   support

        Hypo       1.00      1.00      1.00        31
        Norm       1.00      1.00      1.00        32

    accuracy                           1.00        63
   macro avg       1.00      1.00      1.00        63
weighted avg       1.00      1.00      1.00        63

ss_hcc_svm, ss_hcc_svm_features, ss_hcc_svm_accuracy = feature_selection_svm_rfe(
    X = X_ss_hcc,
    y = y_ss_hcc,
    estimator = LinearSVC(max_iter = 10_000),
    estimator_params = {"C": [0.025, 0.1, 1, 5]},
    n_jobs = -1
)

Training data dimensions: (136, 3000)
Testing data dimensions: (46, 3000)
========================= Training =========================

Best Parameters: {'estimator__C': 0.025}
Best Score (CV avg): 0.9850189600540231
Training accuracy: 1.0

Number of selected genes: 21
Selected genes: ['DDIT4', 'ANGPTL4', 'AKR1C2', 'MMP1', 'CDC20', 'AKR1C3', 'PLAU', 'DKK1', 'PGK1', 'HSPA5', 'LDHA', 'CD44', 'HSPA8', 'ALDOA', 'CAV1', 'TXN', 'MT-CYB', 'TUBA1B', 'NQO1', 'PRDX1', 'PSMD2']
========================= Testing =========================
Confusion matrix:
             Predicted Hypo  Predicted Norm
Actual Hypo              24               1
Actual Norm               0              21
Accuracy: 0.9782608695652174
Classification report:
               precision    recall  f1-score   support

        Hypo       1.00      0.96      0.98        25
        Norm       0.95      1.00      0.98        21

    accuracy                           0.98        46
   macro avg       0.98      0.98      0.98        46
weighted avg       0.98      0.98      0.98        46

ds_mcf7_svm, ds_mcf7_svm_features, ds_mcf7_svm_accuracy = feature_selection_svm_rfe(
    X = X_ds_mcf7,
    y = y_ds_mcf7,
    estimator = LinearSVC(max_iter = 10_000),
    estimator_params = {"C": [0.1, 1, 5]},
    n_jobs = -1
)

Training data dimensions: (16219, 3000)
Testing data dimensions: (5407, 3000)
========================= Training =========================
Best Parameters: {'estimator__C': 0.1}
Best Score (CV avg): 0.9694665289633493
Training accuracy: 0.9858094450744297

Number of selected genes: 380
Selected genes: ['MT-RNR2', 'TFF1', 'MT-RNR1', 'GDF15', 'MT-CO3', 'MT-ND4', 'MT-ND3', 'MT-CYB', 'IGFBP5', 'TMSB10', 'MT-ATP6', 'MT-CO2', 'MT-TS1', 'MT-ND6', 'MT-ND2', 'MTND1P23', 'MT-ND4L', 'MT-ND5', 'MT-TN', 'MT-ND1', 'MT-TA', 'MT-TQ', 'HES1', 'MT-TM', 'LGALS1', 'TMEM64', 'MTND2P28', 'MT-TE', 'H19', 'MT-ATP8', 'H2AC12', 'NCDN', 'MT-TY', 'TOB1', 'H2AC20', 'MT-TP', 'ANKRD52', 'MT-TD', 'C16orf91', 'ATN1', 'WSB2', 'GPM6A', 'ZFP36', 'VMP1', 'TFF3', 'KMT2D', 'FGF23', 'CRTC2', 'CSK', 'PLBD2', 'ITPK1', 'PLEC', 'GOLGA4', 'PLCD3', 'PTP4A2', 'TIAM1', 'SOX4', 'BTBD9', 'H2AC11', 'CBFA2T3', 'PROSER1', 'ARF3', 'PARD6B', 'RPL13', 'TPI1', 'BTN3A2', 'GREM1', 'RNF146', 'S100A10', 'CHAC2', 'ATXN2L', 'TGFB3', 'MGRN1', 'CAPZA1', 'FAM189B', 'GSE1', 'CERS2', 'ENO1', 'SLC48A1', 'PKIB', 'RHOD', 'BLOC1S3', 'KRT19', 'RPL34', 'TCF20', 'LINC01291', 'FAM102A', 'PRRG3', 'GABPB2', 'CAMK2N1', 'VPS9D1-AS1', 'TAF13', 'INCENP', 'ZNRF1', 'NINJ1', 'ZBTB34', 'DSP', 'ZNF480', 'CALHM2', 'MSMB', 'SENP3', 'KCNJ2', 'ZBTB20', 'TPD52L1', 'HSPH1', 'HMGA1', 'CASP8AP2', 'ZNF302', 'ELOA', 'GPATCH4', 'SNX24', 'DVL3', 'SNX27', 'YTHDF3', 'GAB2', 'PACS1', 'NLK', 'THAP1', 'KCNJ3', 'LDLRAP1', 'TRAK2', 'CAMSAP2', 'PPM1G', 'HEPACAM', 'NCALD', 'LRRFIP2', 'DNAJA1', 'SMKR1', 'MAPKAPK2', 'ZNF702P', 'NACC1', 'TRIM37', 'RFK', 'FBXL16', 'TCHP', 'ISCU', 'RABEP1', 'CACNG4', 'RPSAP48', 'WWC3', 'GDAP2', 'SRCAP', 'USP32', 'FLOT2', 'MAFF', 'NCOA1', 'TWNK', 'AKAP5', 'NEDD4L', 'APOOL', 'CCDC18', 'RAB27A', 'BRPF3', 'BCAS3', 'GATAD2A', 'NSD1', 'NPM1P40', 'ANKRD40', 'ILRUN', 'PSMD14', 'STRBP', 'TPM1', 'CAV1', 'MPHOSPH9', 'ANXA6', 'PRXL2C', 'KIF14', 'PYGO2', 'ZNF688', 'KHSRP', 'BAP1', 'MDM2', 'RAB5C', 'PAQR8', 'SOS1', 'KRT80', 'SECISBP2L', 'BOLA3', 'DNAJA4', 'THRB', 'ARPP19', 'S100A11', 'FRS2', 'RGPD4-AS1', 'BRIP1', 'PRR12', 'TEDC2-AS1', 'RPL15', 'DKC1', 'C9orf78', 'NBEAL2', 'SETD3', 'FEM1A', 'SLC25A24', 'ARMC6', 'SLC13A5', 'CFAP97', 'NEDD1', 'PHLDA2', 'MARK3', 'SPATS2L', 'PAPOLA', 'MT2A', 'ZNF354A', 'SET', 'ATXN1L', 'SCYL2', 'ZNF703', 'SRFBP1', 'UBA52', 'MGLL', 'LAD1', 'ZC3H15', 'SLC25A48', 'RAD23A', 'EIF4G2', 'HOXC13', 'PITPNA', 'TAF9B', 'LXN', 'SERINC5', 'FBRS', 'SMC5', 'RAI14', 'TRIM44', 'MYO5C', 'AKT1S1', 'TBKBP1', 'EIF2B4', 'PRR34-AS1', 'PSME4', 'PDAP1', 'ARHGAP26', 'ELP3', 'SENP6', 'DNAJC21', 'FAM104A', 'CS', 'ABL1', 'EIF3A', 'MARK2', 'LCLAT1', 'S100P', 'RCC1L', 'ANKRD17', 'TMEM259', 'CPEB4', 'RAB1B', 'GAPDH', 'TMEM258', 'SSX2IP', 'PDS5A', 'FAM177A1', 'NAA10', 'CNOT9', 'PGK1', 'PKM', 'KLHL8', 'BCL3', 'PRMT6', 'CACNA1A', 'GOLGA3', 'SOCS2', 'HPCAL1', 'PPP1R12B', 'DCTN1', 'C7orf50', 'ZMIZ1', 'PGAM5', 'RPL30', 'ARNTL2', 'PREX1', 'LYAR', 'PRRC2C', 'PCYT1A', 'GLE1', 'ZFC3H1', 'BMPR1B', 'RBBP6', 'ZNF764', 'RAB35', 'ENOX2', 'LMNB2', 'ZNF326', 'ARID1B', 'TIMELESS', 'PFDN4', 'LPP', 'SYNE2', 'ZRANB1', 'PLCB4', 'CBX3', 'NOL4L', 'SPRY1', 'RPS6KA6', 'CKS2', 'SMC6', 'AURKA', 'BICDL1', 'DBNDD1', 'CRNDE', 'C2orf49', 'TPM4', 'FAM111B', 'KPNA2', 'NCKAP1', 'INF2', 'CSNK2A2', 'FARP1', 'FGD5-AS1', 'MAP3K13', 'NCOA5', 'DNAJA3', 'RRP1B', 'HCFC1', 'ACTB', 'GATA3', 'DHX38', 'TSPYL1', 'TBC1D9', 'IWS1', 'FAM50A', 'AFF1', 'WDR43', 'SHISA5', 'CLTB', 'ETF1', 'RSRC2', 'GNAQ', 'BAZ2A', 'TARS1', 'KCNQ1OT1', 'YWHAB', 'EBAG9', 'PITX1', 'KPNA4', 'UTP18', 'PSMD5', 'NFIC', 'PHF20L1', 'PATL1', 'POLB', 'TNIP2', 'ARIH1', 'KLC1', 'ZBTB7A', 'NPLOC4', 'ARFGEF1', 'TRAF3IP2', 'BMS1', 'INPP4B', 'MYH14', 'KITLG', 'ATF5', 'TBCA', 'PICALM', 'FAM13B', 'FBXL18', 'MYO10', 'TAOK3', 'BBOF1', 'CLSPN', 'PAK2', 'STRIP1', 'IFI27L2', 'LTBR', 'BEND7', 'ESRP2', 'C6orf62', 'AAMP', 'PMEPA1', 'UBE2Q2', 'DHX37', 'SLAIN2', 'OTUD7B', 'RPL23', 'NCBP3', 'ATRX', 'NOM1', 'SMIM27']
========================= Testing =========================
Confusion matrix:
             Predicted Hypo  Predicted Norm
Actual Hypo            2140              90
Actual Norm              73            3104
Accuracy: 0.969853893101535
Classification report:
               precision    recall  f1-score   support

        Hypo       0.97      0.96      0.96      2230
        Norm       0.97      0.98      0.97      3177

    accuracy                           0.97      5407
   macro avg       0.97      0.97      0.97      5407
weighted avg       0.97      0.97      0.97      5407

ds_hcc_svm, ds_hcc_svm_features, ds_hcc_svm_accuracy = feature_selection_svm_rfe(
    X = X_ds_hcc,
    y = y_ds_hcc,
    estimator = LinearSVC(max_iter = 10_000),
    estimator_params = {"C": [0.025, 0.1, 1, 5]},
    n_jobs = -1
)

Training data dimensions: (11011, 3000)
Testing data dimensions: (3671, 3000)
========================= Training =========================

Best Parameters: {'estimator__C': 0.025}
Best Score (CV avg): 0.9404403100550717
Training accuracy: 0.9652093585427515

Number of selected genes: 402
Selected genes: ['BCYRN1', 'IGFBP3', 'H2AC11', 'RAPGEF3', 'DDIT4', 'MB', 'MT-TV', 'MT-TL1', 'NDRG1', 'MIR210HG', 'MT-TQ', 'SPN', 'ZNF263', 'MT-CO3', 'CACHD1', 'BTBD9', 'GDPGP1', 'ARTN', 'LINC01304', 'CACNB2', 'HELQ', 'NAXD', 'GPM6A', 'MT-TS1', 'MT-TA', 'CRIP2', 'NEAT1', 'DUSP9', 'PRR5L', 'USP35', 'MT-ND1', 'MT-CYB', 'PHC1', 'H19', 'H2AC12', 'CNR2', 'FGF23', 'MUL1', 'DANT1', 'AKR1C2', 'TMSB10', 'EHBP1L1', 'LDHA', 'MT-ND6', 'EFNA2', 'CITED2', 'H4C5', 'CNOT6L', 'CLDN4', 'CPNE2', 'DTNB', 'GABRE', 'LINC02511', 'MT-ATP6', 'LGALS1', 'NUPR2', 'H2AC16', 'MPDU1', 'ATXN2L', 'PGAM1', 'PPIL1', 'NCL', 'COL6A3', 'ABO', 'RPL17', 'SLC2A1', 'C4orf3', 'NOP10', 'KLC2', 'MT-CO2', 'NCALD', 'EGLN3', 'OPTN', 'ANKRD9', 'TRAK1', 'CNNM2', 'RRAS', 'BNIP3', 'ENTR1', 'FGF8', 'B4GALT1', 'GPI', 'LIMCH1', 'MIR663AHG', 'SREK1IP1P1', 'FBXL17', 'LINC02541', 'P4HA1', 'H2BC4', 'RPL41', 'COX8A', 'MT-TS2', 'PROSER1', 'PGK1', 'CAMK2N1', 'HEPACAM', 'MSR1', 'GDI1', 'SIGMAR1', 'AHNAK2', 'GCAT', 'SINHCAFP3', 'CTXN1', 'LINC01133', 'POLR3GL', 'HES4', 'PDCD4', 'TNFRSF12A', 'ENKD1', 'SHOX', 'RGPD4-AS1', 'HIF3A', 'S100A10', 'APOOL', 'RTL8C', 'ARNTL', 'HMGB2', 'NEDD9', 'TMEM70', 'FASTKD5', 'DAAM1', 'HSP90AB1', 'ZBED2', 'EFNA5', 'PSMG1', 'TMSB4XP4', 'NPM1P40', 'RPL39', 'AJAP1', 'SAMD4A', 'WDR77', 'PAQR7', 'NDUFB4', 'BTN3A2', 'VIT', 'ARHGDIA', 'H3C2', 'FOSL2', 'MIXL1', 'MCM3AP', 'GJB3', 'PRRC2A', 'FSD1L', 'IVL', 'KCNJ3', 'BNIP3L', 'S100A11', 'BMPR1B', 'H2BC9', 'TNNT1', 'CEP120', 'LINC02367', 'RAB30', 'ZBED4', 'RAB11FIP4', 'RNF122', 'NEDD4L', 'RAB2B', 'RPS27', 'CSTB', 'C1orf53', 'NCK1', 'CPEB1', 'MLLT3', 'MELTF-AS1', 'TCF7L1', 'MT1E', 'RPSAP48', 'TNFSF13B', 'ECH1', 'NDUFA8', 'MIOS-DT', 'KRT19', 'ZNF318', 'POLDIP2', 'VPS45', 'ZNF418', 'YTHDF3', 'MT-ND4L', 'PI4KB', 'ADARB1', 'AXL', 'CACNA1A', 'TUBB6', 'NRG4', 'NMD3', 'FAM126B', 'PHACTR1', 'TXNRD2', 'BAP1', 'HSPD1', 'PLD1', 'JAKMIP3', 'DDX23', 'RPL28', 'ANKEF1', 'RPS6KA6', 'DUSP5', 'SH3RF1', 'ARHGEF26', 'SLC6A8', 'JUN', 'OVOL1', 'APEH', 'CAVIN3', 'ZNF302', 'DCAKD', 'ARL2', 'LINC01902', 'RBSN', 'CREB1', 'TATDN2', 'PRRG3', 'RPS21', 'ALDOC', 'MMP2', 'POLE4', 'PTGR1', 'CCDC168', 'GBP1P1', 'TSHZ2', 'IRF2BPL', 'ADM', 'ZBTB20', 'CAST', 'RPS29', 'AKR1C1', 'PCDHGA10', 'RGS10', 'TGDS', 'EPHX1', 'KAT7', 'NEUROD2', 'CFAP251', 'MXRA5', 'PFKFB3', 'PLOD2', 'PPTC7', 'ING2', 'CD47', 'ZNF33B', 'KIRREL1', 'KDM3A', 'UQCC2', 'FUT11', 'MXI1', 'MED18', 'SYNJ2', 'SNHG18', 'RNF25', 'AKT1S1', 'KLLN', 'NCAM1', 'RAB12', 'PDLIM1', 'MT1X', 'DERA', 'YTHDF1', 'AMFR', 'CEP83', 'SF3B4', 'PHRF1', 'GYS1', 'SRA1', 'EPPK1', 'SYT14', 'FAM162A', 'KCNJ2', 'ARMC6', 'MKNK1', 'HSP90AA1', 'INHBA', 'SRSF8', 'FYN', 'BTBD7P1', 'CENPB', 'RHBDD2', 'SNX22', 'SLC2A6', 'LINC01116', 'ISOC2', 'MPHOSPH6', 'JUND', 'RAB3GAP1', 'MNS1', 'DTYMK', 'TOLLIP', 'GIN1', 'FAH', 'GOLGA4', 'TMEM256', 'DGKD', 'WDR43', 'CAMSAP2', 'NACA4P', 'ARHGAP42', 'NDUFC1', 'GAPDH', 'TMEM238', 'GRK2', 'DNAH11', 'ZBTB2', 'TRIM44', 'CIAO2A', 'UTP3', 'CALM2', 'BRMS1', 'PCDHB1', 'TTL', 'FOSL1', 'YKT6', 'ACSL4', 'CCDC34', 'SAT2', 'RHOT2', 'MAD2L1', 'DBT', 'RPL27A', 'RPL37A', 'NUP93', 'AMOTL2', 'PPP4R2', 'CARM1', 'VEGFB', 'NCLN', 'MLLT6', 'MAP2K3', 'DNAAF5', 'PUSL1', 'PSMA7', 'DDX54', 'TCEAL9', 'RPLP0P2', 'KRT4', 'SNORD3B-1', 'FEM1A', 'TRIM52-AS1', 'MCM4', 'CCNG2', 'YWHAZ', 'ARID5B', 'MRPL55', 'KMT2D', 'SPG21', 'ZC3H15', 'EMP2', 'LETM1', 'EIF3J', 'SNRNP70', 'RHOD', 'MAFF', 'MAZ', 'UQCR11', 'PLCE1', 'CPTP', 'ARHGEF7', 'STMN1', 'ZNF202', 'SNHG9', 'HMGA1', 'CLIC1', 'ZHX1', 'TPD52L1', 'FRY', 'DNMT3A', 'ARL13B', 'SMARCB1', 'TWNK', 'RRS1', 'HEY1', 'MRPS2', 'SLC25A48', 'TMEM80', 'DYSF', 'MTA2', 'C19orf53', 'ARSA', 'DGKZ', 'VRK3', 'UIMC1', 'PSIP1', 'ZNF688', 'CMIP', 'PPIG', 'EXOC7', 'TAF15', 'MARCKS', 'AK4', 'KIF5B', 'ATP5F1E', 'IRAK1', 'BRAT1', 'TSR1', 'SART1', 'CAP1', 'SETD2', 'METTL26', 'STC2', 'DDIT3', 'KEAP1', 'DLD', 'CLIP2']
========================= Testing =========================
Confusion matrix:
             Predicted Hypo  Predicted Norm
Actual Hypo            2117             108
Actual Norm              92            1354
Accuracy: 0.9455189321710705
Classification report:
               precision    recall  f1-score   support

        Hypo       0.96      0.95      0.95      2225
        Norm       0.93      0.94      0.93      1446

    accuracy                           0.95      3671
   macro avg       0.94      0.94      0.94      3671
weighted avg       0.95      0.95      0.95      3671

print("SmartSeq MCF7 SVM")
ss_mcf7_pca_svm_pcs = get_selected_pcs_from_model(ss_mcf7_pca_svm.model)
print()

print("SmartSeq HCC SVM")
ss_hcc_pca_svm_pcs = get_selected_pcs_from_model(ss_hcc_pca_svm.model)
print()

print("DropSeq MCF7 SVM")
ds_mcf7_pca_svm_pcs = get_selected_pcs_from_model(ds_mcf7_pca_svm.model)
print()

print("DropSeq HCC SVM")
ds_hcc_pca_svm_pcs = get_selected_pcs_from_model(ds_hcc_pca_svm.model)
print()

SmartSeq MCF7 SVM
Top 8 principal components:
[1, 3, 6, 8, 12, 16, 17, 18]

SmartSeq HCC SVM
Top 11 principal components:
[2, 3, 9, 10, 12, 15, 17, 21, 26, 30, 32]

DropSeq MCF7 SVM
Top 300 principal components:
[1, 2, 3, 5, 6, 8, 15, 16, 17, 18, 19, 25, 26, 27, 28, 29, 30, 31, 32, 33, 36, 37, 40, 43, 44, 45, 46, 52, 55, 56, 57, 60, 61, 62, 65, 66, 69, 71, 74, 81, 82, 85, 87, 88, 91, 92, 94, 95, 99, 100, 104, 105, 107, 110, 112, 114, 115, 116, 118, 119, 120, 121, 127, 128, 135, 138, 140, 141, 142, 145, 146, 147, 149, 153, 157, 160, 161, 167, 170, 172, 173, 175, 177, 181, 188, 190, 191, 193, 195, 198, 200, 201, 203, 204, 205, 206, 211, 212, 213, 218, 219, 230, 231, 232, 234, 235, 239, 243, 245, 247, 249, 252, 254, 255, 257, 260, 263, 264, 267, 271, 273, 275, 279, 281, 282, 286, 287, 291, 293, 302, 305, 312, 317, 318, 319, 320, 322, 323, 327, 329, 332, 339, 341, 342, 344, 348, 350, 352, 353, 355, 361, 362, 364, 370, 371, 375, 380, 383, 385, 387, 389, 391, 392, 393, 398, 399, 400, 401, 402, 406, 408, 409, 411, 415, 418, 419, 429, 431, 433, 434, 435, 436, 438, 442, 449, 455, 457, 459, 460, 461, 462, 464, 466, 469, 470, 471, 481, 483, 484, 485, 486, 487, 491, 494, 495, 496, 497, 499, 504, 507, 508, 510, 512, 515, 517, 518, 519, 520, 522, 527, 534, 538, 540, 541, 543, 546, 552, 555, 556, 557, 564, 565, 571, 576, 579, 580, 582, 585, 591, 594, 596, 597, 598, 599, 602, 603, 606, 610, 612, 615, 621, 623, 626, 627, 631, 632, 633, 642, 644, 646, 647, 650, 652, 653, 655, 658, 661, 667, 672, 674, 675, 677, 681, 682, 687, 696, 698, 700, 702, 705, 711, 718, 722, 724, 726, 729, 732, 733, 734, 736, 741, 742, 743, 745, 746, 751, 754, 755, 756, 758]

DropSeq HCC SVM
Top 343 principal components:
[2, 3, 4, 5, 6, 8, 11, 12, 15, 16, 18, 19, 20, 21, 23, 24, 26, 27, 29, 30, 31, 32, 34, 36, 37, 38, 39, 41, 45, 46, 47, 48, 49, 53, 54, 55, 63, 65, 69, 72, 76, 77, 88, 89, 90, 92, 94, 96, 99, 102, 103, 106, 115, 117, 118, 120, 123, 124, 127, 131, 135, 136, 137, 139, 140, 141, 142, 143, 145, 147, 148, 153, 154, 155, 157, 161, 162, 166, 167, 169, 170, 171, 172, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 187, 189, 190, 191, 193, 197, 198, 200, 201, 205, 208, 210, 213, 215, 217, 218, 219, 220, 221, 224, 225, 227, 229, 230, 231, 234, 235, 237, 238, 239, 240, 243, 247, 249, 253, 254, 255, 259, 260, 261, 262, 263, 265, 266, 269, 270, 272, 275, 282, 290, 292, 294, 295, 297, 300, 301, 303, 305, 308, 310, 312, 313, 315, 318, 319, 323, 325, 326, 329, 332, 334, 336, 338, 339, 341, 345, 350, 351, 356, 357, 358, 359, 360, 362, 369, 371, 372, 373, 374, 375, 377, 379, 380, 382, 383, 384, 391, 393, 396, 399, 401, 402, 408, 409, 412, 413, 414, 415, 420, 427, 429, 434, 438, 439, 443, 444, 445, 447, 448, 450, 451, 452, 453, 457, 461, 474, 479, 488, 490, 494, 497, 503, 507, 509, 510, 515, 516, 521, 523, 525, 528, 534, 539, 540, 541, 543, 550, 551, 553, 561, 562, 563, 564, 565, 566, 567, 575, 576, 577, 581, 582, 583, 584, 586, 593, 594, 597, 598, 599, 600, 601, 603, 608, 609, 610, 612, 613, 622, 630, 632, 637, 640, 641, 642, 644, 646, 651, 656, 657, 667, 672, 674, 685, 689, 691, 692, 693, 698, 699, 700, 701, 705, 713, 714, 716, 717, 718, 730, 733, 746, 747, 749, 755, 756, 758, 760, 761, 763, 766, 769, 771, 777, 778, 781, 783, 784, 785, 789, 792, 793, 794, 799, 807, 808, 809, 810, 812, 813, 814, 815, 820, 828, 829, 831, 832, 835, 839, 842, 843]

ss_mcf7_random_forest, ss_mcf7_random_forest_features, ss_mcf7_random_forest_accuracy = feature_selection_random_forest(
    X = X_ss_mcf7,
    y = y_ss_mcf7,
    estimator = RandomForestClassifier(),
    estimator_params = {
        "n_estimators": [100, 200],
        "max_depth": [5, 10, 20],
        "min_samples_split": [5, 10],
        "min_samples_leaf": [2, 4, 8],
        "max_features": ["sqrt", 0.5],
        "bootstrap": [True],
        "class_weight": ["balanced"]
    },
    n_jobs = -1
)

Training data dimensions: (187, 3000)
Testing data dimensions: (63, 3000)
========================= Training =========================

Best Parameters: {'estimator__bootstrap': True, 'estimator__class_weight': 'balanced', 'estimator__max_depth': 5, 'estimator__max_features': 'sqrt', 'estimator__min_samples_leaf': 2, 'estimator__min_samples_split': 5, 'estimator__n_estimators': 100}
Best Score (CV avg): 0.9945945945945945
Training accuracy: 1.0

Number of selected genes: 54
Selected genes: ['CYP1B1', 'CYP1B1-AS1', 'NDRG1', 'PFKFB3', 'HK2', 'ADM', 'VEGFA', 'BNIP3', 'PFKFB4', 'ENO2', 'MT-CYB', 'SLC9A3R1', 'UBC', 'MT-CO3', 'GPI', 'EMP2', 'MT-CO2', 'DSCAM-AS1', 'PGK1', 'MT-CO1', 'DYNC2I2', 'SLC3A2', 'IFITM3', 'ERO1A', 'DSP', 'IRF2BP2', 'TUBG1', 'MT-ATP6', 'FUT11', 'P4HA1', 'FAM162A', 'PDK1', 'BNIP3L', 'MOV10', 'IFITM2', 'PYCR3', 'FDFT1', 'PFKP', 'ACLY', 'GAPDH', 'FDPS', 'FASN', 'TST', 'APEH', 'PSME2', 'SNRNP25', 'NECTIN2', 'TUBD1', 'MTATP6P1', 'EBP', 'ALDOA', 'CYB561A3', 'ACAT2', 'SQLE']
========================= Testing =========================
Confusion matrix:
             Predicted Hypo  Predicted Norm
Actual Hypo              31               0
Actual Norm               0              32
Accuracy: 1.0
Classification report:
               precision    recall  f1-score   support

        Hypo       1.00      1.00      1.00        31
        Norm       1.00      1.00      1.00        32

    accuracy                           1.00        63
   macro avg       1.00      1.00      1.00        63
weighted avg       1.00      1.00      1.00        63

ss_hcc_random_forest, ss_hcc_random_forest_features, ss_hcc_random_forest_accuracy = feature_selection_random_forest(
    X = X_ss_hcc,
    y = y_ss_hcc,
    estimator = RandomForestClassifier(),
    estimator_params = {
        "n_estimators": [100, 200],
        "max_depth": [5, 10, 20],
        "min_samples_split": [5, 10],
        "min_samples_leaf": [2, 4, 8],
        "max_features": ["sqrt", 0.5],
        "bootstrap": [True],
        "class_weight": ["balanced"]
    },
    n_jobs = -1
)

Training data dimensions: (136, 3000)
Testing data dimensions: (46, 3000)
========================= Training =========================

Best Parameters: {'estimator__bootstrap': True, 'estimator__class_weight': 'balanced', 'estimator__max_depth': 5, 'estimator__max_features': 'sqrt', 'estimator__min_samples_leaf': 2, 'estimator__min_samples_split': 5, 'estimator__n_estimators': 100}
Best Score (CV avg): 0.9924263674614305
Training accuracy: 0.9926147162639153

Number of selected genes: 64
Selected genes: ['DDIT4', 'ANGPTL4', 'NDRG1', 'EGLN3', 'CA9', 'PLIN2', 'UPK1B', 'FAM83A', 'BNIP3', 'INSIG1', 'KRT19', 'BHLHE40', 'ALDOC', 'GPRC5A', 'KCTD11', 'SLC2A1', 'PGK1', 'SLC6A8', 'LOXL2', 'CDKN1A', 'PFKFB3', 'LDHA', 'ARRDC3', 'ADM', 'BUB1B', 'HILPDA', 'LBH', 'BUB1', 'FOSL2', 'KYNU', 'ASB2', 'ERO1A', 'EIF5', 'P4HA1', 'C1orf116', 'RALGDS', 'SNX33', 'MOB3A', 'GPI', 'CALB1', 'ALDOA', 'BLCAP', 'PLOD2', 'ZNF473', 'HES1', 'GYS1', 'ENO2', 'TMEM45A', 'BNIP3L', 'PLAC8', 'TPBG', 'C4orf3', 'EGLN1', 'PRSS8', 'FAM13A', 'SRM', 'HSPH1', 'MIF', 'LDHB', 'PPP1R3G', 'FUT11', 'FAM162A', 'KDM3A', 'P4HA2']
========================= Testing =========================
Confusion matrix:
             Predicted Hypo  Predicted Norm
Actual Hypo              24               1
Actual Norm               0              21
Accuracy: 0.9782608695652174
Classification report:
               precision    recall  f1-score   support

        Hypo       1.00      0.96      0.98        25
        Norm       0.95      1.00      0.98        21

    accuracy                           0.98        46
   macro avg       0.98      0.98      0.98        46
weighted avg       0.98      0.98      0.98        46

ds_mcf7_random_forest, ds_mcf7_random_forest_features, ds_mcf7_random_forest_accuracy = feature_selection_random_forest(
    X = X_ds_mcf7,
    y = y_ds_mcf7,
    estimator = RandomForestClassifier(),
    estimator_params = {
        "n_estimators": [200, 500, 1000],
        "max_depth": [20, 50, None],
        "min_samples_split": [2, 5],
        "min_samples_leaf": [1, 2],
        "max_features": ["sqrt", 0.5, 0.8],
        "bootstrap": [True, False],
        "class_weight": ["balanced", None]
    },
    n_jobs = -1
)

Training data dimensions: (16219, 3000)
Testing data dimensions: (5407, 3000)
========================= Training =========================
Best Parameters: {'estimator__n_estimators': 1000, 'estimator__min_samples_split': 5, 'estimator__min_samples_leaf': 2, 'estimator__max_features': 'sqrt', 'estimator__max_depth': 50, 'estimator__class_weight': 'balanced', 'estimator__bootstrap': True}
Best Score (CV avg): 0.96612717826348
Training accuracy: 0.9963072692759114

Number of selected genes: 72
Selected genes: ['MALAT1', 'MT-RNR2', 'TFF1', 'MT-RNR1', 'H4C3', 'MT-CO3', 'MT-ND4', 'MT-ND3', 'MT-CYB', 'TMSB10', 'MT-ATP6', 'MT-CO2', 'BCYRN1', 'RPS5', 'HES1', 'LGALS1', 'TMEM64', 'DSCAM-AS1', 'RPL12', 'RPS12', 'TOB1', 'RPL39', 'RPS16', 'TFF3', 'FGF23', 'RPL35', 'SOX4', 'RPS19', 'RPLP2', 'RPL36', 'PARD6B', 'RPL13', 'TPI1', 'S100A10', 'RPS28', 'FTL', 'RPL35A', 'ENO1', 'KRT19', 'RPS14', 'RPL34', 'DSP', 'UQCRQ', 'RPS15A', 'ROMO1', 'ELOB', 'KRT8', 'RPS15', 'ATP5ME', 'S100A11', 'ATP5MK', 'NDUFB2', 'RPL15', 'SNRPD2', 'RPS27', 'SET', 'UBA52', 'RPL37A', 'KRT18', 'GAPDH', 'TMEM258', 'PGK1', 'PKM', 'RPL30', 'ACTB', 'RPL11', 'HSPB1', 'RPLP1', 'SERF2', 'COX7A2', 'COX7C', 'RPL23']
========================= Testing =========================
Confusion matrix:
             Predicted Hypo  Predicted Norm
Actual Hypo            2111             119
Actual Norm              54            3123
Accuracy: 0.9680044386905863
Classification report:
               precision    recall  f1-score   support

        Hypo       0.98      0.95      0.96      2230
        Norm       0.96      0.98      0.97      3177

    accuracy                           0.97      5407
   macro avg       0.97      0.96      0.97      5407
weighted avg       0.97      0.97      0.97      5407

ds_hcc_random_forest, ds_hcc_random_forest_features, ds_hcc_random_forest_accuracy = feature_selection_random_forest(
    X = X_ds_hcc,
    y = y_ds_hcc,
    estimator = RandomForestClassifier(),
    estimator_params = {
        "n_estimators": [200, 500, 1000],
        "max_depth": [20, 50, None],
        "min_samples_split": [2, 5],
        "min_samples_leaf": [1, 2],
        "max_features": ["sqrt", 0.5, 0.8],
        "bootstrap": [True, False],
        "class_weight": ["balanced", None]
    },
    n_jobs = -1
)

Training data dimensions: (11011, 3000)
Testing data dimensions: (3671, 3000)
========================= Training =========================

Best Parameters: {'estimator__n_estimators': 1000, 'estimator__min_samples_split': 5, 'estimator__min_samples_leaf': 2, 'estimator__max_features': 'sqrt', 'estimator__max_depth': 50, 'estimator__class_weight': 'balanced', 'estimator__bootstrap': True}
Best Score (CV avg): 0.9260927654063373
Training accuracy: 0.9988589561630253

Number of selected genes: 112
Selected genes: ['MALAT1', 'MT-RNR2', 'BCYRN1', 'IGFBP3', 'H1-3', 'H4C3', 'HSPA5', 'PLEC', 'HSP90B1', 'NDRG1', 'MT-TQ', 'BTBD9', 'ENO1', 'GPM6A', 'HNRNPA2B1', 'NEAT1', 'H2AC12', 'H1-1', 'FGF23', 'AKR1C2', 'TMSB10', 'RPS28', 'LDHA', 'RPS5', 'PDIA3', 'NCL', 'NCALD', 'EGLN3', 'CNNM2', 'BNIP3', 'B4GALT1', 'EZR', 'P4HA1', 'RPL41', 'PGK1', 'AHNAK2', 'RPS19', 'RPL35', 'S100A10', 'SERF2', 'CENPF', 'HSP90AB1', 'RPL12', 'TMSB4X', 'POLR2L', 'NPM1P40', 'RPL39', 'PKM', 'KCNJ3', 'BNIP3L', 'S100A11', 'HNRNPU', 'RPS27', 'RPLP2', 'RPLP1', 'KRT19', 'RPS2', 'TPI1', 'TPT1', 'CACNA1A', 'BAP1', 'HSPD1', 'RPL28', 'ZNF302', 'GSTP1', 'EEF2', 'PRRG3', 'MT2A', 'CAST', 'S100A6', 'RPL36', 'AKR1C1', 'DSP', 'ATAD2', 'RPSA', 'ELOB', 'RPS8', 'CBX3', 'RPL21', 'HSP90AA1', 'WDR43', 'GAPDH', 'RPS3', 'TRIM44', 'TPX2', 'CALM2', 'FOSL1', 'PTMS', 'RPL27A', 'RPL37A', 'RPL8', 'UQCRQ', 'PSMA7', 'HNRNPM', 'EEF1A1', 'YWHAZ', 'RPL37', 'RPL10', 'ZC3H15', 'EIF3J', 'STMN1', 'PABPC1', 'HMGA1', 'ATP5MG', 'DNMT1', 'RPL13', 'CAV1', 'C19orf53', 'MARCKS', 'ATP5F1E', 'RPS14', 'RAC1']
========================= Testing =========================
Confusion matrix:
             Predicted Hypo  Predicted Norm
Actual Hypo            2127              98
Actual Norm             170            1276
Accuracy: 0.9269953691092345
Classification report:
               precision    recall  f1-score   support

        Hypo       0.93      0.96      0.94      2225
        Norm       0.93      0.88      0.90      1446

    accuracy                           0.93      3671
   macro avg       0.93      0.92      0.92      3671
weighted avg       0.93      0.93      0.93      3671

print("SmartSeq MCF7 Random Forest")
ss_mcf7_pca_random_forest_pcs = get_selected_pcs_from_model(ss_mcf7_pca_random_forest.model)
print()

print("SmartSeq HCC Random Forest")
ss_hcc_pca_random_forest_pcs = get_selected_pcs_from_model(ss_hcc_pca_random_forest.model)
print()

print("DropSeq MCF7 Random Forest")
ds_mcf7_pca_random_forest_pcs = get_selected_pcs_from_model(ds_mcf7_pca_random_forest.model)
print()

print("DropSeq HCC Random Forest")
ds_hcc_pca_random_forest_pcs = get_selected_pcs_from_model(ds_hcc_pca_random_forest.model)
print()

SmartSeq MCF7 Random Forest
Top 7 principal components:
[1, 2, 3, 4, 5, 6, 9]

SmartSeq HCC Random Forest
Top 3 principal components:
[2, 3, 4]

DropSeq MCF7 Random Forest
Top 82 principal components:
[1, 2, 3, 4, 5, 6, 7, 8, 9, 12, 15, 16, 17, 18, 19, 21, 23, 25, 26, 27, 28, 32, 33, 35, 36, 37, 38, 47, 48, 55, 60, 82, 95, 97, 110, 116, 121, 133, 140, 142, 144, 145, 147, 149, 151, 157, 167, 170, 232, 236, 278, 297, 300, 301, 303, 307, 314, 317, 318, 320, 321, 322, 326, 328, 331, 335, 338, 344, 358, 367, 377, 379, 380, 381, 383, 403, 422, 424, 442, 458, 474, 475]

DropSeq HCC Random Forest
Top 104 principal components:
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 23, 24, 26, 30, 31, 34, 35, 36, 37, 38, 39, 41, 42, 44, 45, 46, 47, 48, 49, 53, 54, 56, 60, 63, 65, 68, 69, 70, 72, 73, 74, 75, 76, 78, 80, 90, 99, 100, 102, 103, 106, 109, 110, 126, 127, 133, 134, 141, 142, 145, 147, 155, 156, 161, 167, 169, 170, 172, 174, 176, 182, 184, 185, 187, 189, 190, 192, 195, 196, 197, 198, 201, 202, 208, 212, 240, 250, 259, 262, 270, 283, 290, 301, 317]

logit_feature_lists = [ss_mcf7_logit_features, ss_hcc_logit_features, ds_mcf7_logit_features, ds_hcc_logit_features]
top_logit_features_occurrences = count_and_sort_occurrences(logit_feature_lists, False)
top_logit_features = {}

for i in range(4, 0, -1):
    top_logit_features[i] = top_logit_features.get(i + 1, []) + filter_by_occurrences(top_logit_features_occurrences, i)

for i in range(4, 0, -1):
    if len(top_logit_features[i]) == 0:
        continue
    print(f"{len(top_logit_features[i])} gene(s) selected for logistic regression across {i}+ data set(s):")
    print(top_logit_features[i])
    print()

3 gene(s) selected for logistic regression across 4+ data set(s):
['MT-CO3', 'PGK1', 'MT-CYB']

10 gene(s) selected for logistic regression across 3+ data set(s):
['MT-CO3', 'PGK1', 'MT-CYB', 'DDIT4', 'LDHA', 'MT-CO2', 'MT-ATP6', 'TMSB10', 'HMGA1', 'KRT19']

95 gene(s) selected for logistic regression across 2+ data set(s):
['MT-CO3', 'PGK1', 'MT-CYB', 'DDIT4', 'LDHA', 'MT-CO2', 'MT-ATP6', 'TMSB10', 'HMGA1', 'KRT19', 'TRIM44', 'MT-TA', 'GPM6A', 'FUT11', 'MT-ND6', 'NDRG1', 'MT-RNR1', 'BAP1', 'PLOD2', 'IGFBP3', 'MT-ND4L', 'TUBA1B', 'HSPH1', 'CSTB', 'AURKA', 'ATXN2L', 'MT-ND1', 'GPI', 'EGLN3', 'BMPR1B', 'BNIP3', 'CAMK2N1', 'NCALD', 'CACNA1A', 'CAV1', 'DHCR7', 'PFKFB3', 'S100A11', 'C4orf3', 'S100A10', 'GOLGA4', 'GATA3', 'GAPDH', 'BTN3A2', 'BTBD9', 'KCNJ2', 'KCNJ3', 'RPSAP48', 'MT-TS1', 'MT-TQ', 'TPD52L1', 'BNIP3L', 'RPS6KA6', 'NEDD4L', 'ALDOC', 'SLC6A8', 'AMOTL2', 'PROSER1', 'FOSL2', 'ALDOA', 'NPM1P40', 'AKT1S1', 'ZC3H15', 'H2AC12', 'AKR1C2', 'AKR1C1', 'EMP2', 'HES1', 'ZNF302', 'CKS2', 'PRRG3', 'ADM', 'CLDN4', 'FAM162A', 'ZNF688', 'P4HA1', 'MAFF', 'H2AC11', 'CAMSAP2', 'YTHDF3', 'RHOD', 'ARMC6', 'KMT2D', 'SLC25A48', 'KPNA2', 'HSP90AA1', 'LGALS1', 'KRT4', 'SLC2A1', 'FEM1A', 'H19', 'FGF23', 'WDR43', 'APOOL', 'RGPD4-AS1']

858 gene(s) selected for logistic regression across 1+ data set(s):
['MT-CO3', 'PGK1', 'MT-CYB', 'DDIT4', 'LDHA', 'MT-CO2', 'MT-ATP6', 'TMSB10', 'HMGA1', 'KRT19', 'TRIM44', 'MT-TA', 'GPM6A', 'FUT11', 'MT-ND6', 'NDRG1', 'MT-RNR1', 'BAP1', 'PLOD2', 'IGFBP3', 'MT-ND4L', 'TUBA1B', 'HSPH1', 'CSTB', 'AURKA', 'ATXN2L', 'MT-ND1', 'GPI', 'EGLN3', 'BMPR1B', 'BNIP3', 'CAMK2N1', 'NCALD', 'CACNA1A', 'CAV1', 'DHCR7', 'PFKFB3', 'S100A11', 'C4orf3', 'S100A10', 'GOLGA4', 'GATA3', 'GAPDH', 'BTN3A2', 'BTBD9', 'KCNJ2', 'KCNJ3', 'RPSAP48', 'MT-TS1', 'MT-TQ', 'TPD52L1', 'BNIP3L', 'RPS6KA6', 'NEDD4L', 'ALDOC', 'SLC6A8', 'AMOTL2', 'PROSER1', 'FOSL2', 'ALDOA', 'NPM1P40', 'AKT1S1', 'ZC3H15', 'H2AC12', 'AKR1C2', 'AKR1C1', 'EMP2', 'HES1', 'ZNF302', 'CKS2', 'PRRG3', 'ADM', 'CLDN4', 'FAM162A', 'ZNF688', 'P4HA1', 'MAFF', 'H2AC11', 'CAMSAP2', 'YTHDF3', 'RHOD', 'ARMC6', 'KMT2D', 'SLC25A48', 'KPNA2', 'HSP90AA1', 'LGALS1', 'KRT4', 'SLC2A1', 'FEM1A', 'H19', 'FGF23', 'WDR43', 'APOOL', 'RGPD4-AS1', 'GPATCH4', 'GRK2', 'GJB3', 'H2BC4', 'GLE1', 'H2AX', 'GNAQ', 'H2AC20', 'H1-0', 'GPX2', 'GPRC5A', 'GOLGA3', 'H2AC16', 'GSE1', 'GYS1', 'GREM1', 'GAB2', 'GIN1', 'FBRS', 'FLOT2', 'FLNA', 'FGFBP1', 'FGF8', 'FDFT1', 'FBXL18', 'FBXL17', 'FBXL16', 'FASTKD5', 'GFRA1', 'FARP1', 'FAM83A', 'FAM50A', 'FAM189B', 'FAM177A1', 'FAM13B', 'FAM126B', 'FAM111B', 'FN1', 'FOS', 'FOSL1', 'FRS2', 'GDPGP1', 'GDI1', 'GDF15', 'GDAP2', 'GCAT', 'GBP1P1', 'GATAD2A', 'GABRE', 'GABPB2', 'H3C2', 'FYN', 'FYB1', 'FTL', 'FTH1', 'FSD1L', 'FSCN1', 'FRY', 'H2BC9', 'IFI27L2', 'H4C3', 'KITLG', 'KRT80', 'KRT8', 'KRT18', 'KPNA4', 'KNSTRN', 'KLLN', 'KLHL8', 'KLC2', 'KLC1', 'KIRREL1', 'H4C5', 'KIF5B', 'KIF2C', 'KIF23', 'KIF14', 'KHSRP', 'KEAP1', 'KDM5B', 'KDM3A', 'KCTD11', 'KYNU', 'LAD1', 'LAMB3', 'LCLAT1', 'LXN', 'LTBR', 'LRRFIP2', 'LPP', 'LOXL2', 'LMNB2', 'LMNA', 'LINC02541', 'LINC02511', 'LINC02367', 'LINC01902', 'LINC01304', 'LINC01291', 'LINC01133', 'LINC01116', 'LIMCH1', 'LETM1', 'LDLRAP1', 'LDHB', 'KCNQ1OT1', 'KAT7', 'JUP', 'FAM102A', 'ID3', 'HSPD1', 'HSPA8', 'HSPA5', 'HSP90B1', 'HSP90AB1', 'HRH1', 'HOXC13', 'HMGCS1', 'HMGB2', 'HLA-A', 'HIF3A', 'HEY1', 'HES4', 'HERPUD1', 'HEPACAM', 'HELQ', 'HCFC1', 'HBP1', 'IER2', 'IGFBP5', 'JUND', 'ILRUN', 'JUNB', 'JUN', 'JAKMIP3', 'IWS1', 'IVL', 'ITPK1', 'ITGA6', 'ISOC2', 'ISG15', 'ISCU', 'IRF6', 'IRF2BPL', 'IRAK1', 'INSIG1', 'INPP4B', 'INHBA', 'ING2', 'INF2', 'INCENP', 'FAM104A', 'ZWINT', 'FAH', 'BRIP1', 'C7orf50', 'C6orf62', 'C2orf49', 'C1orf53', 'C19orf53', 'C16orf91', 'C10orf55', 'BTBD7P1', 'BRPF3', 'BRMS1', 'BRAT1', 'CDC25B', 'BOLA3', 'BMS1', 'BLOC1S3', 'BLCAP', 'BIRC5', 'BICDL1', 'BHLHE40', 'BCYRN1', 'BCL3', 'BCAS3', 'C9orf78', 'CA9', 'CACHD1', 'CACNB2', 'CD47', 'CD44', 'CCNG2', 'CCNB2', 'CCNB1', 'CCM2', 'CCDC34', 'CCDC18', 'CCDC168', 'CBX3', 'CBFA2T3', 'CAVIN3', 'CAST', 'CASP8AP2', 'CARM1', 'CAPZA1', 'CAP1', 'CANX', 'CALM2', 'CALHM2', 'CACNG4', 'BBOF1', 'BAZ2A', 'BAG3', 'APEH', 'ANKRD9', 'ANKRD52', 'ANKRD40', 'ANKRD17', 'ANKEF1', 'ANGPTL4', 'AMFR', 'ALDH1A3', 'AKR1C3', 'AKAP5', 'AK4', 'AJAP1', 'AHNAK2', 'AFF1', 'ADARB1', 'ADAP1', 'ACTB', 'ACSL4', 'ACAT2', 'ABO', 'ABL1', 'ANXA6', 'ARF3', 'B4GALT1', 'ARFGEF1', 'AXL', 'ATXN1L', 'ATRX', 'ATP5F1E', 'ATN1', 'ATF5', 'ARTN', 'ARSA', 'ARPP19', 'ARNTL2', 'ARNTL', 'ARL2', 'ARL13B', 'ARIH1', 'ARID5B', 'ARID1B', 'ARHGEF7', 'ARHGEF26', 'ARHGDIA', 'ARHGAP42', 'ARHGAP26', 'CDC20', 'CDC6', 'F3', 'DNAJA3', 'DYSF', 'DVL3', 'DUSP9', 'DUSP5', 'DTYMK', 'DTNB', 'DSP', 'DNMT3A', 'DNAJC21', 'DNAJA4', 'DNAJA1', 'CDK2AP2', 'DNAH11', 'DNAAF5', 'DLD', 'DKK1', 'DKC1', 'DHX38', 'DHX37', 'DHRS3', 'DGKZ', 'DGKD', 'EBAG9', 'ECH1', 'EFNA2', 'EFNA5', 'EXOC7', 'ETF1', 'ESRP2', 'ERO1A', 'EPPK1', 'EPHX1', 'ENTR1', 'ENOX2', 'ENO1', 'ENKD1', 'ELP3', 'ELOA', 'EIF5', 'EIF4G2', 'EIF4A2', 'EIF3J', 'EIF3A', 'EIF2B4', 'EHBP1L1', 'LYAR', 'EGLN1', 'DERA', 'DDX54', 'DDX5', 'CNR2', 'CNOT6L', 'CNNM2', 'CMIP', 'CLTB', 'CLSPN', 'CLIP2', 'CLIC1', 'CLDN7', 'CKAP2', 'CITED2', 'CIAO2A', 'CHAC2', 'CHAC1', 'CFAP97', 'CFAP251', 'CERS2', 'CEP83', 'CEP120', 'CENPB', 'CEACAM5', 'CDKN1A', 'CNOT9', 'COL6A3', 'DDX23', 'COX8A', 'DDIT3', 'DCTN1', 'DCBLD2', 'DCAKD', 'DBT', 'DBNDD1', 'DANT1', 'DAAM1', 'CYP1B1', 'CXCL1', 'CTXN1', 'CSNK2A2', 'CSK', 'CS', 'CRTC2', 'CRNDE', 'CRIP2', 'CREB1', 'CPTP', 'CPNE2', 'CPEB1', 'LY6D', 'MIF-AS1', 'MAD2L1', 'SOCS2', 'SRA1', 'SQSTM1', 'SPRY1', 'SPP1', 'SPN', 'SPG21', 'SPATS2L', 'SPAG5', 'SOX4', 'SOS1', 'SNX27', 'SREK1IP1P1', 'SNX24', 'SNX22', 'SNRNP70', 'SNORD3B-1', 'SNHG9', 'SNHG18', 'SMKR1', 'SMIM27', 'SMC6', 'SMC5', 'SRCAP', 'SRFBP1', 'MAP2K3', 'TAF13', 'TCF20', 'TCEAL9', 'TBKBP1', 'TBCA', 'TBC1D9', 'TATDN2', 'TARS1', 'TAOK3', 'TAF9B', 'TAF15', 'SYT14', 'SRM', 'SYNJ2', 'SYNE2', 'SULF2', 'STRIP1', 'STRBP', 'STMN1', 'STC2', 'STARD10', 'SSX2IP', 'SRXN1', 'SMARCB1', 'SLCO4A1', 'SLC9A3R1', 'RPLP0P2', 'S100P', 'S100A2', 'RTL8C', 'RSRC2', 'RRS1', 'RRP1B', 'RRAS', 'RPS29', 'RPS27', 'RPS21', 'RPL41', 'SLC48A1', 'RPL39', 'RPL37A', 'RPL34', 'RPL30', 'RPL28', 'RPL27A', 'RPL23', 'RPL17', 'RPL15', 'RPL13', 'SAMD4A', 'SART1', 'SAT2', 'SCD', 'SLC39A6', 'SLC38A2', 'SLC2A6', 'SLC25A24', 'SLC20A1', 'SLC13A5', 'SLAIN2', 'SINHCAFP3', 'SIGMAR1', 'SHOX', 'SHISA5', 'SH3RF1', 'SF3B4', 'SETD3', 'SETD2', 'SET', 'SERINC5', 'SENP6', 'SEMA4B', 'SECISBP2L', 'SCYL2', 'TCF7L1', 'TCHP', 'TEDC2-AS1', 'VIT', 'YKT6', 'XBP1', 'WWC3', 'WTAPP1', 'WSB2', 'WDR77', 'VRK3', 'VPS9D1-AS1', 'VPS45', 'VMP1', 'VEGFB', 'UBB', 'VCPIP1', 'UTP3', 'UTP18', 'USP35', 'USP32', 'UQCR11', 'UQCC2', 'UPK1B', 'UIMC1', 'UGDH', 'YTHDF1', 'YWHAB', 'YWHAZ', 'ZBED2', 'ZNRF1', 'ZNF764', 'ZNF703', 'ZNF702P', 'ZNF480', 'ZNF418', 'ZNF354A', 'ZNF33B', 'ZNF326', 'ZNF318', 'ZNF263', 'ZNF202', 'ZMIZ1', 'ZHX1', 'ZFP36', 'ZFC3H1', 'ZBTB7A', 'ZBTB34', 'ZBTB20', 'ZBTB2', 'ZBED4', 'UBE2Q2', 'UBA52', 'TFF1', 'TMEM258', 'TOB1', 'TNNT1', 'TNIP2', 'TNFSF13B', 'TNFRSF12A', 'TMSB4XP4', 'TMEM80', 'TMEM70', 'TMEM64', 'TMEM259', 'TMEM256', 'TYSND1', 'TMEM238', 'TIMELESS', 'TIAM1', 'THRB', 'THBS1', 'THAP1', 'TGFB3', 'TGDS', 'TFRC', 'TFF3', 'TOLLIP', 'TPBG', 'TPI1', 'TPM1', 'TXNRD2', 'TXNIP', 'TXN', 'TWNK', 'TUBB6', 'TUBB4B', 'TUBB', 'TTL', 'TSR1', 'TSPYL1', 'TSPO', 'TSHZ2', 'TRIM52-AS1', 'TRIM37', 'TRIM29', 'TRIM16', 'TRAK2', 'TRAK1', 'TRAF3IP2', 'TPX2', 'TPM4', 'RNF25', 'RNF146', 'RNF122', 'MYO5C', 'NCKAP1', 'NCK1', 'NCDN', 'NCBP3', 'NCAM1', 'NBEAL2', 'NAXD', 'NACC1', 'NACA4P', 'NAA10', 'MYO10', 'MT-TV', 'MYH14', 'MYC', 'MXRA5', 'MXI1', 'MTND2P28', 'MTND1P23', 'MTA2', 'MT2A', 'MT1X', 'MT1E', 'NCL', 'NCLN', 'NCOA1', 'NCOA5', 'NSD1', 'NRP1', 'NRG4', 'NQO1', 'NPLOC4', 'NOP10', 'NOM1', 'NOLC1', 'NOL4L', 'NME1-NME2', 'NMD3', 'NLK', 'NINJ1', 'NFIC', 'NEUROD2', 'NEDD9', 'NEDD1', 'NEAT1', 'NDUFC1', 'NDUFB4', 'NDUFA8', 'MT-TY', 'MT-TS2', 'NUP188', 'MELTF-AS1', 'MKNK1', 'MIXL1', 'MIR663AHG', 'MIR210HG', 'MIOS-DT', 'ZRANB1', 'MIF', 'MGRN1', 'MGLL', 'METTL26', 'MED18', 'MT-TP', 'MDM2', 'MCM4', 'MCM3AP', 'MB', 'MAZ', 'MARK3', 'MARK2', 'MARCKS', 'MAPKAPK2', 'MAP3K13', 'MLLT3', 'MLLT6', 'MMP1', 'MMP2', 'MT-TN', 'MT-TM', 'MT-TL1', 'MT-TE', 'MT-TD', 'MT-RNR2', 'MT-ND5', 'MT-ND4', 'MT-ND3', 'MT-ND2', 'MT-CO1', 'MT-ATP8', 'MSR1', 'MSMO1', 'MSMB', 'MRPL55', 'MRNIP', 'MPHOSPH9', 'MPHOSPH6', 'MPDU1', 'MNS1', 'NT5C', 'NUP93', 'RHOT2', 'PRR12', 'PSMD2', 'PSMD14', 'PSMA7', 'PSIP1', 'PRXL2C', 'PRSS23', 'PRRC2C', 'PRRC2A', 'PRR5L', 'PRR34-AS1', 'PRNP', 'POLR3GL', 'PRMT6', 'PREX1', 'PRDX1', 'PRC1', 'PPTC7', 'PPP4R2', 'PPP1R12B', 'PPM1G', 'PPIL1', 'PPIG', 'PSMD5', 'PSME4', 'PSMG1', 'PTGR1', 'RHBDD2', 'RGS10', 'RFK', 'RCC1L', 'RBSN', 'RBBP6', 'RAPGEF3', 'RAI14', 'RAD23A', 'RABEP1', 'RAB5C', 'RAB3GAP1', 'RAB35', 'RAB30', 'RAB2B', 'RAB27A', 'RAB1B', 'RAB12', 'RAB11FIP4', 'PYGO2', 'PTP4A2', 'PPIF', 'POLR3A', 'NUPR2', 'PCDH1', 'PFDN4', 'PERP', 'PDS5A', 'PDLIM1', 'PDCD4', 'PDAP1', 'PCYT1A', 'PCNA', 'PCDHGA10', 'PCDHB1', 'PATL1', 'POLR2A', 'PARD6B', 'PAQR8', 'PAQR7', 'PAPOLA', 'PAK2', 'PACS1', 'P4HA2', 'OVOL1', 'OTUD7B', 'OPTN', 'PGAM1', 'PGAM5', 'PHACTR1', 'PHF20L1', 'POLE4', 'POLDIP2', 'POLB', 'PMEPA1', 'PLK2', 'PLIN2', 'PLEC', 'PLD1', 'PLCE1', 'PLCD3', 'PLCB4', 'PLBD2', 'PLAU', 'PKM', 'PKIB', 'PITX1', 'PITPNA', 'PICALM', 'PI4KB', 'PHRF1', 'PHLDA2', 'AAMP']

svm_feature_lists = [ss_mcf7_svm_features, ss_hcc_svm_features, ds_mcf7_svm_features, ds_hcc_svm_features]
top_svm_features_occurrences = count_and_sort_occurrences(svm_feature_lists, False)
top_svm_features = {}

for i in range(4, 0, -1):
    top_svm_features[i] = top_svm_features.get(i + 1, []) + filter_by_occurrences(top_svm_features_occurrences, i)

for i in range(4, 0, -1):
    if len(top_svm_features[i]) == 0:
        continue
    print(f"{len(top_svm_features[i])} gene(s) selected for SVM across {i}+ data set(s):")
    print(top_svm_features[i])
    print()

2 gene(s) selected for SVM across 4+ data set(s):
['PGK1', 'MT-CYB']

7 gene(s) selected for SVM across 3+ data set(s):
['PGK1', 'MT-CYB', 'LDHA', 'TMSB10', 'DDIT4', 'MT-CO3', 'MT-CO2']

70 gene(s) selected for SVM across 2+ data set(s):
['PGK1', 'MT-CYB', 'LDHA', 'TMSB10', 'DDIT4', 'MT-CO3', 'MT-CO2', 'S100A10', 'RPSAP48', 'TPD52L1', 'RPS6KA6', 'BAP1', 'ATXN2L', 'NEDD4L', 'TRIM44', 'BMPR1B', 'MT-TS1', 'MT-TQ', 'KRT19', 'TWNK', 'FGF23', 'FEM1A', 'MT-TA', 'KMT2D', 'TMEM64', 'KCNJ3', 'KCNJ2', 'HMGA1', 'NPM1P40', 'H2AC12', 'H2AC11', 'H19', 'STC2', 'CAV1', 'GPM6A', 'SLC25A48', 'GOLGA4', 'CAMSAP2', 'CAMK2N1', 'CACNA1A', 'BTN3A2', 'BTBD9', 'GATA3', 'GAPDH', 'NCALD', 'S100A11', 'ARMC6', 'HEPACAM', 'DSP', 'MAFF', 'RHOD', 'ZBTB20', 'YTHDF3', 'ZNF302', 'FAM162A', 'ZC3H15', 'MT-ATP6', 'AKT1S1', 'ZNF688', 'MT-ND6', 'AKR1C2', 'PRRG3', 'RGPD4-AS1', 'APOOL', 'WDR43', 'EMP2', 'MT-ND1', 'PROSER1', 'LGALS1', 'MT-ND4L']

757 gene(s) selected for SVM across 1+ data set(s):
['PGK1', 'MT-CYB', 'LDHA', 'TMSB10', 'DDIT4', 'MT-CO3', 'MT-CO2', 'S100A10', 'RPSAP48', 'TPD52L1', 'RPS6KA6', 'BAP1', 'ATXN2L', 'NEDD4L', 'TRIM44', 'BMPR1B', 'MT-TS1', 'MT-TQ', 'KRT19', 'TWNK', 'FGF23', 'FEM1A', 'MT-TA', 'KMT2D', 'TMEM64', 'KCNJ3', 'KCNJ2', 'HMGA1', 'NPM1P40', 'H2AC12', 'H2AC11', 'H19', 'STC2', 'CAV1', 'GPM6A', 'SLC25A48', 'GOLGA4', 'CAMSAP2', 'CAMK2N1', 'CACNA1A', 'BTN3A2', 'BTBD9', 'GATA3', 'GAPDH', 'NCALD', 'S100A11', 'ARMC6', 'HEPACAM', 'DSP', 'MAFF', 'RHOD', 'ZBTB20', 'YTHDF3', 'ZNF302', 'FAM162A', 'ZC3H15', 'MT-ATP6', 'AKT1S1', 'ZNF688', 'MT-ND6', 'AKR1C2', 'PRRG3', 'RGPD4-AS1', 'APOOL', 'WDR43', 'EMP2', 'MT-ND1', 'PROSER1', 'LGALS1', 'MT-ND4L', 'GOLGA3', 'FAM111B', 'GNAQ', 'FAM104A', 'GPATCH4', 'GPI', 'GLE1', 'FAM126B', 'FBXL18', 'GREM1', 'FAM102A', 'FAH', 'H4C5', 'H3C2', 'H2BC9', 'H2BC4', 'H2AC20', 'H2AC16', 'EPHX1', 'EPPK1', 'ESRP2', 'GYS1', 'GSE1', 'ETF1', 'GRK2', 'GIN1', 'EXOC7', 'GJB3', 'FAM13B', 'GDPGP1', 'FRY', 'FBXL17', 'FGD5-AS1', 'FGF8', 'FBXL16', 'FLNA', 'FLOT2', 'FOS', 'FOSL1', 'HCFC1', 'FRS2', 'FBRS', 'FASTKD5', 'FARP1', 'FSD1L', 'GDI1', 'FUT11', 'FYN', 'FAM50A', 'FAM189B', 'FAM177A1', 'GAB2', 'GABPB2', 'GABRE', 'GATAD2A', 'GBP1P1', 'GCAT', 'GDAP2', 'GDF15', 'FOSL2', 'ZRANB1', 'HELQ', 'KRT80', 'LINC01291', 'LINC01133', 'LINC01116', 'LIMCH1', 'LETM1', 'LDLRAP1', 'LCLAT1', 'LAD1', 'KRT4', 'HES1', 'KPNA4', 'KPNA2', 'KLLN', 'KLHL8', 'KLC2', 'KLC1', 'KITLG', 'KIRREL1', 'LINC01304', 'LINC01902', 'LINC02367', 'LINC02511', 'MCM3AP', 'MB', 'MAZ', 'MARK3', 'MARK2', 'MARCKS', 'MAPKAPK2', 'MAP3K13', 'MAP2K3', 'MAD2L1', 'LYAR', 'LXN', 'LTBR', 'LRRFIP2', 'LPP', 'LMNB2', 'LINC02541', 'KIF5B', 'KIF14', 'KHSRP', 'IGFBP5', 'ENOX2', 'IFITM3', 'IFI27L2', 'HSPH1', 'HSPD1', 'HSPA8', 'HSPA5', 'HSP90AB1', 'HSP90AA1', 'HPCAL1', 'HOXC13', 'HNRNPA2B1', 'HMGB2', 'HILPDA', 'HIF3A', 'HEY1', 'HES4', 'IGFBP3', 'ILRUN', 'KEAP1', 'INCENP', 'KDM3A', 'KCNQ1OT1', 'KAT7', 'JUND', 'JUN', 'JAKMIP3', 'IWS1', 'IVL', 'ITPK1', 'ISOC2', 'ISCU', 'IRF2BPL', 'IRAK1', 'INPP4B', 'INHBA', 'ING2', 'INF2', 'ENTR1', 'DYNC2I2', 'ENO1', 'BRIP1', 'BOLA3', 'BNIP3L', 'BNIP3', 'BMS1', 'BLOC1S3', 'BICDL1', 'BEND7', 'BCYRN1', 'BCL3', 'BCAS3', 'BBOF1', 'BAZ2A', 'B4GALT1', 'AXL', 'AURKA', 'ATXN1L', 'ATRX', 'ATP9A', 'ATP5F1E', 'BRAT1', 'BRMS1', 'ENKD1', 'BRPF3', 'CAST', 'CASP8AP2', 'CARM1', 'CAPZA1', 'CAP1', 'CALM2', 'CALHM2', 'CACNG4', 'CACNB2', 'CACHD1', 'C9orf78', 'C7orf50', 'C6orf62', 'C4orf3', 'C2orf49', 'C1orf53', 'C19orf53', 'C16orf91', 'BTBD7P1', 'ATN1', 'ATF5', 'ARTN', 'ARSA', 'ANKEF1', 'ANGPTL4', 'AMOTL2', 'AMFR', 'ALDOC', 'ALDOA', 'AKR1C3', 'AKR1C1', 'AKAP5', 'AK4', 'AJAP1', 'AHNAK2', 'AFF1', 'ADM', 'ADARB1', 'ACTB', 'ACSL4', 'ABO', 'ABL1', 'ANKRD17', 'ANKRD40', 'ANKRD52', 'ARID1B', 'ARPP19', 'ARPC1B', 'ARNTL2', 'ARNTL', 'ARL2', 'ARL13B', 'ARIH1', 'ARID5B', 'ARHGEF7', 'ANKRD9', 'ARHGEF26', 'ARHGDIA', 'ARHGAP42', 'ARHGAP26', 'ARFGEF1', 'ARF3', 'APEH', 'ANXA6', 'CAVIN3', 'CBFA2T3', 'CBX3', 'DAAM1', 'DNAJA3', 'DNAJA1', 'DNAH11', 'DNAAF5', 'DLD', 'DKK1', 'DKC1', 'DHX38', 'DHX37', 'DGKZ', 'DGKD', 'DERA', 'DDX54', 'DDX23', 'DDIT3', 'DCTN1', 'DCAKD', 'DBT', 'DBNDD1', 'DNAJA4', 'DNAJC21', 'DNMT3A', 'EFNA5', 'ELP3', 'ELOA', 'EIF4G2', 'EIF3J', 'EIF3A', 'EIF2B4', 'EHBP1L1', 'EGLN3', 'EFNA2', 'DTNB', 'ECH1', 'EBAG9', 'DYSF', 'MCM7', 'DVL3', 'DUSP9', 'DUSP5', 'DTYMK', 'DANT1', 'CTXN1', 'CCDC168', 'CSTB', 'CLIC1', 'CLDN4', 'CKS2', 'CITED2', 'CIAO2A', 'CHAC2', 'CFAP97', 'CFAP251', 'CERS2', 'CEP83', 'CEP120', 'CENPB', 'CDC20', 'CD9', 'CD47', 'CD44', 'CCNG2', 'CCDC34', 'CCDC18', 'CLIP2', 'CLSPN', 'CLTB', 'CPTP', 'CSNK2A2', 'CSK', 'CS', 'CRTC2', 'CRNDE', 'CRIP2', 'CREB1', 'CRABP2', 'CPNE2', 'CMIP', 'CPEB4', 'CPEB1', 'COX8A', 'COL6A3', 'CNR2', 'CNOT9', 'CNOT6L', 'CNNM2', 'MCM4', 'MPHOSPH6', 'MDM2', 'SPG21', 'SOX4', 'SOS1', 'SOCS2', 'SNX27', 'SNX24', 'SNX22', 'SNRNP70', 'SNORD3B-1', 'SNHG9', 'SNHG18', 'SMKR1', 'SMIM27', 'SMC6', 'SMC5', 'SMARCB1', 'SLC6A8', 'SLC48A1', 'SLC2A6', 'SLC2A1', 'SPATS2L', 'SPN', 'MED18', 'SPRY1', 'TAOK3', 'TAF9B', 'TAF15', 'TAF13', 'SYTL2', 'SYT14', 'SYNJ2', 'SYNE2', 'SULF2', 'STRIP1', 'STRBP', 'STMN1', 'STARD10', 'SSX2IP', 'SRSF8', 'SRFBP1', 'SREK1IP1P1', 'SRCAP', 'SRA1', 'SLC25A24', 'SLC13A5', 'SLAIN2', 'SINHCAFP3', 'RPS29', 'RPS27', 'RPS21', 'RPLP0P2', 'RPL41', 'RPL39', 'RPL37A', 'RPL34', 'RPL30', 'RPL28', 'RPL27A', 'RPL23', 'RPL17', 'RPL15', 'RPL13', 'RNF25', 'RNF146', 'RNF122', 'RHOT2', 'RRAS', 'RRP1B', 'RRS1', 'SERINC5', 'SIGMAR1', 'SHOX', 'SHISA5', 'SH3RF1', 'SF3B4', 'SETD3', 'SETD2', 'SET', 'SENP6', 'RSRC2', 'SENP3', 'SECISBP2L', 'SCYL2', 'SAT2', 'SART1', 'SAMD4A', 'S100P', 'RTL8C', 'TARS1', 'TATDN2', 'TBC1D9', 'UBA52', 'YTHDF1', 'YKT6', 'XBP1', 'WWC3', 'WSB2', 'WDR77', 'VRK3', 'VPS9D1-AS1', 'VPS45', 'VMP1', 'VIT', 'VEGFB', 'UTP3', 'UTP18', 'USP35', 'USP32', 'UQCR11', 'UQCC2', 'UIMC1', 'YWHAB', 'YWHAZ', 'ZBED2', 'ZNF318', 'ZNF764', 'ZNF703', 'ZNF702P', 'ZNF480', 'ZNF418', 'ZNF354A', 'ZNF33B', 'ZNF326', 'ZNF263', 'ZBED4', 'ZNF202', 'ZMIZ1', 'ZHX1', 'ZFP36', 'ZFC3H1', 'ZBTB7A', 'ZBTB34', 'ZBTB2', 'UBE2Q2', 'TXNRD2', 'TBCA', 'TXN', 'TMEM70', 'TMEM259', 'TMEM258', 'TMEM256', 'TMEM238', 'TIMELESS', 'TIAM1', 'THRB', 'THAP1', 'TGFB3', 'TGDS', 'TFF3', 'TFF1', 'TEDC2-AS1', 'TCHP', 'TCF7L1', 'TCF20', 'TCEAL9', 'TBKBP1', 'TMEM80', 'TMSB4XP4', 'TNFRSF12A', 'TRAK2', 'TUBB6', 'TUBA1B', 'TTL', 'TSR1', 'TSPYL1', 'TSHZ2', 'TRIM52-AS1', 'TRIM37', 'TRAK1', 'TNFSF13B', 'TRAF3IP2', 'TPM4', 'TPM1', 'TPI1', 'TOLLIP', 'TOB1', 'TNNT1', 'TNIP2', 'RHBDD2', 'RGS10', 'RFK', 'MTND2P28', 'NCOA5', 'NCOA1', 'NCLN', 'NCL', 'NCKAP1', 'NCK1', 'NCDN', 'NCBP3', 'NCAM1', 'NBEAL2', 'NAXD', 'NACC1', 'NACA4P', 'NAA10', 'MYO5C', 'MYO10', 'MYH14', 'MXRA5', 'MXI1', 'NDRG1', 'NDUFA8', 'NDUFB4', 'NOL4L', 'NUP93', 'NSD1', 'NRG4', 'NR4A1', 'NQO1', 'NPLOC4', 'NOP10', 'NOM1', 'NME1-NME2', 'NDUFC1', 'NMD3', 'NLK', 'NINJ1', 'NFIC', 'NEUROD2', 'NEDD9', 'NEDD1', 'NEAT1', 'MUL1', 'MTND1P23', 'OPTN', 'MTA2', 'MRPS2', 'MRPL55', 'MPHOSPH9', 'ZNRF1', 'MPDU1', 'MNS1', 'MMP2', 'MMP1', 'MLLT6', 'MLLT3', 'MKNK1', 'MIXL1', 'MIR663AHG', 'MIR210HG', 'MIOS-DT', 'MGRN1', 'MGLL', 'METTL26', 'MELTF-AS1', 'MSMB', 'MSR1', 'MT-ATP8', 'MT-TM', 'MT2A', 'MT1X', 'MT1E', 'MT-TY', 'MT-TV', 'MT-TS2', 'MT-TP', 'MT-TN', 'MT-TL1', 'MT-CO1', 'MT-TE', 'MT-TD', 'MT-RNR2', 'MT-RNR1', 'MT-ND5', 'MT-ND4', 'MT-ND3', 'MT-ND2', 'NUPR2', 'OTUD7B', 'RCC1L', 'PPIG', 'PSMD5', 'PSMD2', 'PSMD14', 'PSMA7', 'PSIP1', 'PSAP', 'PRXL2C', 'PRRC2C', 'PRRC2A', 'PRR5L', 'PRR34-AS1', 'PRR12', 'PRMT6', 'PREX1', 'PRDX1', 'PPTC7', 'PPP4R2', 'PPP1R12B', 'PPM1G', 'PSME4', 'PSMG1', 'PTGR1', 'RAB35', 'RBSN', 'RBBP6', 'RAPGEF3', 'RAI14', 'RAD23A', 'RABEP1', 'RAB5C', 'RAB3GAP1', 'RAB30', 'PTP4A2', 'RAB2B', 'RAB27A', 'RAB1B', 'RAB12', 'RAB11FIP4', 'QSOX1', 'PYGO2', 'PUSL1', 'PPIL1', 'POLR3GL', 'OVOL1', 'POLE4', 'PGAM5', 'PGAM1', 'PFKFB3', 'PFDN4', 'PDS5A', 'PDLIM1', 'PDCD4', 'PDAP1', 'PCYT1A', 'PCDHGA10', 'PCDHB1', 'PATL1', 'PARD6B', 'PAQR8', 'PAQR7', 'PAPOLA', 'PAK2', 'PACS1', 'P4HA1', 'PHACTR1', 'PHC1', 'PHF20L1', 'PLCB4', 'POLDIP2', 'POLB', 'PMEPA1', 'PLOD2', 'PLEC', 'PLD1', 'PLCE1', 'PLCD3', 'PLBD2', 'PHLDA2', 'PLAU', 'PKM', 'PKIB', 'PITX1', 'PITPNA', 'PICALM', 'PI4KB', 'PHRF1', 'AAMP']

random_forest_feature_lists = [ss_mcf7_random_forest_features, ss_hcc_random_forest_features, ds_mcf7_random_forest_features, ds_hcc_random_forest_features]
top_random_forest_features_occurrences = count_and_sort_occurrences(random_forest_feature_lists, False)
top_random_forest_features = {}

for i in range(4, 0, -1):
    top_random_forest_features[i] = top_random_forest_features.get(i + 1, []) + filter_by_occurrences(top_random_forest_features_occurrences, i)

for i in range(4, 0, -1):
    if len(top_random_forest_features[i]) == 0:
        continue
    print(f"{len(top_random_forest_features[i])} gene(s) selected for random forest across {i}+ data set(s):")
    print(top_random_forest_features[i])
    print()

1 gene(s) selected for random forest across 4+ data set(s):
['PGK1']

8 gene(s) selected for random forest across 3+ data set(s):
['PGK1', 'NDRG1', 'BNIP3', 'DSP', 'P4HA1', 'BNIP3L', 'KRT19', 'GAPDH']

51 gene(s) selected for random forest across 2+ data set(s):
['PGK1', 'NDRG1', 'BNIP3', 'DSP', 'P4HA1', 'BNIP3L', 'KRT19', 'GAPDH', 'RPS28', 'RPS27', 'RPS19', 'FUT11', 'RPS5', 'S100A10', 'S100A11', 'RPS14', 'GPI', 'RPLP2', 'RPLP1', 'RPL37A', 'RPL39', 'H4C3', 'RPL36', 'PFKFB3', 'RPL35', 'DSCAM-AS1', 'PKM', 'FGF23', 'RPL13', 'RPL12', 'EGLN3', 'ELOB', 'ENO1', 'ENO2', 'ERO1A', 'SERF2', 'FAM162A', 'MT-CO3', 'MT-CYB', 'TPI1', 'HES1', 'TMSB10', 'LDHA', 'MT-ATP6', 'BCYRN1', 'MT-CO2', 'ALDOA', 'MALAT1', 'ADM', 'UQCRQ', 'MT-RNR2']

242 gene(s) selected for random forest across 1+ data set(s):
['PGK1', 'NDRG1', 'BNIP3', 'DSP', 'P4HA1', 'BNIP3L', 'KRT19', 'GAPDH', 'RPS28', 'RPS27', 'RPS19', 'FUT11', 'RPS5', 'S100A10', 'S100A11', 'RPS14', 'GPI', 'RPLP2', 'RPLP1', 'RPL37A', 'RPL39', 'H4C3', 'RPL36', 'PFKFB3', 'RPL35', 'DSCAM-AS1', 'PKM', 'FGF23', 'RPL13', 'RPL12', 'EGLN3', 'ELOB', 'ENO1', 'ENO2', 'ERO1A', 'SERF2', 'FAM162A', 'MT-CO3', 'MT-CYB', 'TPI1', 'HES1', 'TMSB10', 'LDHA', 'MT-ATP6', 'BCYRN1', 'MT-CO2', 'ALDOA', 'MALAT1', 'ADM', 'UQCRQ', 'MT-RNR2', 'FDPS', 'INSIG1', 'HSPB1', 'FOSL1', 'IGFBP3', 'HSPD1', 'FAM83A', 'IFITM3', 'HSPH1', 'FASN', 'FDFT1', 'HSPA5', 'IFITM2', 'H2AC12', 'HSP90AB1', 'FOSL2', 'HSP90B1', 'H1-3', 'HILPDA', 'FAM13A', 'H1-1', 'HK2', 'GYS1', 'HMGA1', 'HNRNPA2B1', 'GSTP1', 'GPRC5A', 'GPM6A', 'HNRNPM', 'HNRNPU', 'HSP90AA1', 'FTL', 'ZNF473', 'DYNC2I2', 'EZR', 'EMP2', 'C19orf53', 'BUB1B', 'BUB1', 'BTBD9', 'BLCAP', 'BHLHE40', 'BAP1', 'B4GALT1', 'ATP5MK', 'ATP5MG', 'ATP5ME', 'ATP5F1E', 'ATAD2', 'ASB2', 'ARRDC3', 'APEH', 'ANGPTL4', 'ALDOC', 'AKR1C2', 'AKR1C1', 'AHNAK2', 'ACTB', 'ACLY', 'C1orf116', 'C4orf3', 'CA9', 'CYP1B1', 'EIF5', 'EIF3J', 'EGLN1', 'EEF2', 'EEF1A1', 'EBP', 'KCNJ3', 'DNMT1', 'DDIT4', 'CYP1B1-AS1', 'CYB561A3', 'CACNA1A', 'COX7C', 'COX7A2', 'CNNM2', 'CENPF', 'CDKN1A', 'CBX3', 'CAV1', 'CAST', 'CALM2', 'CALB1', 'IRF2BP2', 'MOV10', 'KCTD11', 'KDM3A', 'SNRPD2', 'SNRNP25', 'SLC9A3R1', 'SLC6A8', 'SLC3A2', 'SLC2A1', 'SET', 'S100A6', 'RPSA', 'RPS8', 'RPS3', 'RPS2', 'RPS16', 'RPS15A', 'RPS15', 'RPS12', 'RPL8', 'RPL41', 'RPL37', 'RPL35A', 'RPL34', 'RPL30', 'RPL28', 'SNX33', 'SOX4', 'SQLE', 'TRIM44', 'ZC3H15', 'YWHAZ', 'WDR43', 'VEGFA', 'UPK1B', 'UBC', 'UBA52', 'TUBG1', 'TUBD1', 'TST', 'TPX2', 'SRM', 'TPT1', 'TPBG', 'TOB1', 'TMSB4X', 'TMEM64', 'TMEM45A', 'TMEM258', 'TFF3', 'TFF1', 'STMN1', 'RPL27A', 'RPL23', 'RPL21', 'MT-CO1', 'NEAT1', 'NDUFB2', 'NCL', 'NCALD', 'MTATP6P1', 'MT2A', 'MT-TQ', 'MT-RNR1', 'MT-ND4', 'MT-ND3', 'ZNF302', 'NPM1P40', 'MOB3A', 'MIF', 'MARCKS', 'LOXL2', 'LGALS1', 'LDHB', 'LBH', 'KYNU', 'KRT8', 'KRT18', 'NECTIN2', 'P4HA2', 'RPL15', 'PRRG3', 'RPL11', 'RPL10', 'ROMO1', 'RALGDS', 'RAC1', 'PYCR3', 'PTMS', 'PSME2', 'PSMA7', 'PRSS8', 'PPP1R3G', 'PABPC1', 'POLR2L', 'PLOD2', 'PLIN2', 'PLEC', 'PLAC8', 'PFKP', 'PFKFB4', 'PDK1', 'PDIA3', 'PARD6B', 'ACAT2']

feature_lists = logit_feature_lists + svm_feature_lists + random_forest_feature_lists
top_genes_occurrences = count_and_sort_occurrences(feature_lists, False)
top_genes = {}

for i in range(12, 0, -1):
    top_genes[i] = top_genes.get(i + 1, []) + filter_by_occurrences(top_genes_occurrences, i)

for i in range(12, 0, -1):
    if len(top_genes[i]) == 0:
        continue
    print(f"{len(top_genes[i])} gene(s) selected {i}+ times:")
    print(top_genes[i])
    print()

1 gene(s) selected 12+ times:
['PGK1']

1 gene(s) selected 11+ times:
['PGK1']

2 gene(s) selected 10+ times:
['PGK1', 'MT-CYB']

3 gene(s) selected 9+ times:
['PGK1', 'MT-CYB', 'MT-CO3']

7 gene(s) selected 8+ times:
['PGK1', 'MT-CYB', 'MT-CO3', 'MT-CO2', 'TMSB10', 'KRT19', 'LDHA']

10 gene(s) selected 7+ times:
['PGK1', 'MT-CYB', 'MT-CO3', 'MT-CO2', 'TMSB10', 'KRT19', 'LDHA', 'MT-ATP6', 'GAPDH', 'DDIT4']

20 gene(s) selected 6+ times:
['PGK1', 'MT-CYB', 'MT-CO3', 'MT-CO2', 'TMSB10', 'KRT19', 'LDHA', 'MT-ATP6', 'GAPDH', 'DDIT4', 'BNIP3L', 'HMGA1', 'P4HA1', 'BNIP3', 'NDRG1', 'FGF23', 'DSP', 'S100A11', 'S100A10', 'FAM162A']

45 gene(s) selected 5+ times:
['PGK1', 'MT-CYB', 'MT-CO3', 'MT-CO2', 'TMSB10', 'KRT19', 'LDHA', 'MT-ATP6', 'GAPDH', 'DDIT4', 'BNIP3L', 'HMGA1', 'P4HA1', 'BNIP3', 'NDRG1', 'FGF23', 'DSP', 'S100A11', 'S100A10', 'FAM162A', 'AKR1C2', 'MT-TQ', 'H2AC12', 'KCNJ3', 'FUT11', 'CAV1', 'BAP1', 'TRIM44', 'CACNA1A', 'WDR43', 'ADM', 'NPM1P40', 'LGALS1', 'PFKFB3', 'EGLN3', 'GPI', 'EMP2', 'ZNF302', 'HES1', 'NCALD', 'GPM6A', 'BTBD9', 'ZC3H15', 'ALDOA', 'PRRG3']

97 gene(s) selected 4+ times:
['PGK1', 'MT-CYB', 'MT-CO3', 'MT-CO2', 'TMSB10', 'KRT19', 'LDHA', 'MT-ATP6', 'GAPDH', 'DDIT4', 'BNIP3L', 'HMGA1', 'P4HA1', 'BNIP3', 'NDRG1', 'FGF23', 'DSP', 'S100A11', 'S100A10', 'FAM162A', 'AKR1C2', 'MT-TQ', 'H2AC12', 'KCNJ3', 'FUT11', 'CAV1', 'BAP1', 'TRIM44', 'CACNA1A', 'WDR43', 'ADM', 'NPM1P40', 'LGALS1', 'PFKFB3', 'EGLN3', 'GPI', 'EMP2', 'ZNF302', 'HES1', 'NCALD', 'GPM6A', 'BTBD9', 'ZC3H15', 'ALDOA', 'PRRG3', 'CAMK2N1', 'BCYRN1', 'GATA3', 'RPSAP48', 'GOLGA4', 'PROSER1', 'BMPR1B', 'FOSL2', 'CAMSAP2', 'RPS6KA6', 'FEM1A', 'RGPD4-AS1', 'RHOD', 'RPL13', 'TPI1', 'TPD52L1', 'BTN3A2', 'RPS27', 'ENO1', 'C4orf3', 'TMEM64', 'H19', 'HSPH1', 'H2AC11', 'NEDD4L', 'MT-ND1', 'ZNF688', 'MT-ND4L', 'MT-ND6', 'MT-RNR1', 'MT-RNR2', 'MT-TA', 'AKR1C1', 'AKT1S1', 'ALDOC', 'MT-TS1', 'ATXN2L', 'YTHDF3', 'MAFF', 'APOOL', 'KCNJ2', 'PLOD2', 'HSP90AA1', 'RPL39', 'ARMC6', 'SLC6A8', 'IGFBP3', 'PKM', 'SLC25A48', 'KMT2D', 'SLC2A1', 'RPL37A']

155 gene(s) selected 3+ times:
['PGK1', 'MT-CYB', 'MT-CO3', 'MT-CO2', 'TMSB10', 'KRT19', 'LDHA', 'MT-ATP6', 'GAPDH', 'DDIT4', 'BNIP3L', 'HMGA1', 'P4HA1', 'BNIP3', 'NDRG1', 'FGF23', 'DSP', 'S100A11', 'S100A10', 'FAM162A', 'AKR1C2', 'MT-TQ', 'H2AC12', 'KCNJ3', 'FUT11', 'CAV1', 'BAP1', 'TRIM44', 'CACNA1A', 'WDR43', 'ADM', 'NPM1P40', 'LGALS1', 'PFKFB3', 'EGLN3', 'GPI', 'EMP2', 'ZNF302', 'HES1', 'NCALD', 'GPM6A', 'BTBD9', 'ZC3H15', 'ALDOA', 'PRRG3', 'CAMK2N1', 'BCYRN1', 'GATA3', 'RPSAP48', 'GOLGA4', 'PROSER1', 'BMPR1B', 'FOSL2', 'CAMSAP2', 'RPS6KA6', 'FEM1A', 'RGPD4-AS1', 'RHOD', 'RPL13', 'TPI1', 'TPD52L1', 'BTN3A2', 'RPS27', 'ENO1', 'C4orf3', 'TMEM64', 'H19', 'HSPH1', 'H2AC11', 'NEDD4L', 'MT-ND1', 'ZNF688', 'MT-ND4L', 'MT-ND6', 'MT-RNR1', 'MT-RNR2', 'MT-TA', 'AKR1C1', 'AKT1S1', 'ALDOC', 'MT-TS1', 'ATXN2L', 'YTHDF3', 'MAFF', 'APOOL', 'KCNJ2', 'PLOD2', 'HSP90AA1', 'RPL39', 'ARMC6', 'SLC6A8', 'IGFBP3', 'PKM', 'SLC25A48', 'KMT2D', 'SLC2A1', 'RPL37A', 'CNNM2', 'SOX4', 'SET', 'CSTB', 'CLDN4', 'CKS2', 'RPL41', 'EIF3J', 'ERO1A', 'RPL34', 'HSPA5', 'MT-ND3', 'MT-ND4', 'MT2A', 'MARCKS', 'NCL', 'NEAT1', 'KRT4', 'KPNA2', 'PARD6B', 'KDM3A', 'HSPD1', 'PLEC', 'RPL30', 'HSP90AB1', 'HEPACAM', 'H4C3', 'STMN1', 'GYS1', 'PSMA7', 'FOSL1', 'RPL15', 'RPL23', 'RPL27A', 'RPL28', 'STC2', 'MT-CO1', 'ANGPTL4', 'TMEM258', 'YWHAZ', 'UBA52', 'C19orf53', 'ATP5F1E', 'APEH', 'TOB1', 'TFF3', 'AMOTL2', 'CBX3', 'ZBTB20', 'AHNAK2', 'AURKA', 'TWNK', 'ACTB', 'B4GALT1', 'CALM2', 'TFF1', 'CAST', 'TUBA1B']

784 gene(s) selected 2+ times:
['PGK1', 'MT-CYB', 'MT-CO3', 'MT-CO2', 'TMSB10', 'KRT19', 'LDHA', 'MT-ATP6', 'GAPDH', 'DDIT4', 'BNIP3L', 'HMGA1', 'P4HA1', 'BNIP3', 'NDRG1', 'FGF23', 'DSP', 'S100A11', 'S100A10', 'FAM162A', 'AKR1C2', 'MT-TQ', 'H2AC12', 'KCNJ3', 'FUT11', 'CAV1', 'BAP1', 'TRIM44', 'CACNA1A', 'WDR43', 'ADM', 'NPM1P40', 'LGALS1', 'PFKFB3', 'EGLN3', 'GPI', 'EMP2', 'ZNF302', 'HES1', 'NCALD', 'GPM6A', 'BTBD9', 'ZC3H15', 'ALDOA', 'PRRG3', 'CAMK2N1', 'BCYRN1', 'GATA3', 'RPSAP48', 'GOLGA4', 'PROSER1', 'BMPR1B', 'FOSL2', 'CAMSAP2', 'RPS6KA6', 'FEM1A', 'RGPD4-AS1', 'RHOD', 'RPL13', 'TPI1', 'TPD52L1', 'BTN3A2', 'RPS27', 'ENO1', 'C4orf3', 'TMEM64', 'H19', 'HSPH1', 'H2AC11', 'NEDD4L', 'MT-ND1', 'ZNF688', 'MT-ND4L', 'MT-ND6', 'MT-RNR1', 'MT-RNR2', 'MT-TA', 'AKR1C1', 'AKT1S1', 'ALDOC', 'MT-TS1', 'ATXN2L', 'YTHDF3', 'MAFF', 'APOOL', 'KCNJ2', 'PLOD2', 'HSP90AA1', 'RPL39', 'ARMC6', 'SLC6A8', 'IGFBP3', 'PKM', 'SLC25A48', 'KMT2D', 'SLC2A1', 'RPL37A', 'CNNM2', 'SOX4', 'SET', 'CSTB', 'CLDN4', 'CKS2', 'RPL41', 'EIF3J', 'ERO1A', 'RPL34', 'HSPA5', 'MT-ND3', 'MT-ND4', 'MT2A', 'MARCKS', 'NCL', 'NEAT1', 'KRT4', 'KPNA2', 'PARD6B', 'KDM3A', 'HSPD1', 'PLEC', 'RPL30', 'HSP90AB1', 'HEPACAM', 'H4C3', 'STMN1', 'GYS1', 'PSMA7', 'FOSL1', 'RPL15', 'RPL23', 'RPL27A', 'RPL28', 'STC2', 'MT-CO1', 'ANGPTL4', 'TMEM258', 'YWHAZ', 'UBA52', 'C19orf53', 'ATP5F1E', 'APEH', 'TOB1', 'TFF3', 'AMOTL2', 'CBX3', 'ZBTB20', 'AHNAK2', 'AURKA', 'TWNK', 'ACTB', 'B4GALT1', 'CALM2', 'TFF1', 'CAST', 'TUBA1B', 'IVL', 'IWS1', 'ARIH1', 'IRF2BPL', 'ITPK1', 'ISOC2', 'ISCU', 'IRAK1', 'INSIG1', 'INPP4B', 'JAKMIP3', 'ARID1B', 'JUN', 'KIF5B', 'KLLN', 'KLHL8', 'KLC2', 'KLC1', 'KITLG', 'KIRREL1', 'ARHGEF26', 'KIF14', 'JUND', 'KHSRP', 'KEAP1', 'ARHGEF7', 'KCTD11', 'KCNQ1OT1', 'ARID5B', 'KAT7', 'INHBA', 'CDKN1A', 'ING2', 'HMGB2', 'HIF3A', 'HEY1', 'HES4', 'ATRX', 'HELQ', 'HCFC1', 'H4C5', 'ATXN1L', 'H3C2', 'H2BC9', 'H2BC4', 'H2AC20', 'H2AC16', 'AXL', 'BAZ2A', 'GSE1', 'GRK2', 'HILPDA', 'HNRNPA2B1', 'INF2', 'HOXC13', 'INCENP', 'ILRUN', 'IGFBP5', 'ARHGAP42', 'ARL13B', 'ARL2', 'IFITM3', 'IFI27L2', 'ARNTL', 'ARNTL2', 'HSPA8', 'ARPP19', 'HSP90B1', 'ARSA', 'ARTN', 'ATF5', 'ATN1', 'ARHGDIA', 'ARF3', 'ARHGAP26', 'MARK3', 'MLLT6', 'MLLT3', 'MKNK1', 'MIXL1', 'MIR663AHG', 'MIR210HG', 'MIOS-DT', 'MIF', 'MGRN1', 'MGLL', 'METTL26', 'MELTF-AS1', 'MED18', 'MDM2', 'MCM4', 'MCM3AP', 'MB', 'MMP1', 'AMFR', 'AKR1C3', 'MSMB', 'ABL1', 'ABO', 'ACAT2', 'ACSL4', 'MSR1', 'ADARB1', 'AFF1', 'AJAP1', 'MMP2', 'AK4', 'AKAP5', 'MRPL55', 'MPHOSPH9', 'MPHOSPH6', 'MPDU1', 'ZRANB1', 'MAZ', 'MARK2', 'KPNA4', 'MAPKAPK2', 'LINC01291', 'LINC01133', 'LINC01116', 'LIMCH1', 'ANKRD9', 'LETM1', 'LDLRAP1', 'ANXA6', 'LDHB', 'LCLAT1', 'LAD1', 'KYNU', 'KRT80', 'KRT8', 'BBOF1', 'ARFGEF1', 'KRT18', 'LINC01304', 'LINC01902', 'LINC02367', 'LXN', 'MAP3K13', 'MAP2K3', 'MALAT1', 'ANKEF1', 'MAD2L1', 'LYAR', 'ANKRD17', 'LTBR', 'LINC02511', 'LRRFIP2', 'LPP', 'ANKRD40', 'LOXL2', 'LMNB2', 'ANKRD52', 'LINC02541', 'GREM1', 'BCL3', 'GPRC5A', 'DNAJA3', 'DNAH11', 'DNAAF5', 'DLD', 'DKK1', 'DKC1', 'DHX38', 'DHX37', 'CAVIN3', 'DHCR7', 'DGKZ', 'DGKD', 'DERA', 'DDX54', 'CBFA2T3', 'DDX23', 'DDIT3', 'DCTN1', 'DNAJA1', 'DNAJA4', 'DBT', 'DNAJC21', 'CAPZA1', 'CARM1', 'CASP8AP2', 'EGLN1', 'EFNA5', 'EFNA2', 'ECH1', 'EBAG9', 'DYSF', 'DYNC2I2', 'DVL3', 'DUSP9', 'DUSP5', 'DTYMK', 'DTNB', 'DSCAM-AS1', 'DNMT3A', 'DCAKD', 'DBNDD1', 'EIF2B4', 'CNOT6L', 'CMIP', 'CLTB', 'CLSPN', 'CLIP2', 'CLIC1', 'CCNG2', 'CD44', 'CITED2', 'CD47', 'CDC20', 'CIAO2A', 'CHAC2', 'CFAP97', 'CFAP251', 'CERS2', 'CEP83', 'CEP120', 'CCDC34', 'CNOT9', 'DANT1', 'CNR2', 'DAAM1', 'CYP1B1', 'CTXN1', 'CCDC168', 'CSNK2A2', 'CSK', 'CS', 'CRTC2', 'CRNDE', 'CRIP2', 'CREB1', 'CCDC18', 'CPTP', 'CPNE2', 'CPEB1', 'COX8A', 'COL6A3', 'EHBP1L1', 'EIF3A', 'BCAS3', 'GAB2', 'BLOC1S3', 'FTL', 'FSD1L', 'FRY', 'FRS2', 'BMS1', 'BOLA3', 'FOS', 'FLOT2', 'FLNA', 'FGF8', 'BRAT1', 'BRIP1', 'FDFT1', 'BRMS1', 'FBXL18', 'FBXL17', 'FYN', 'GABPB2', 'FBXL16', 'GABRE', 'GPATCH4', 'CENPB', 'BHLHE40', 'GOLGA3', 'GNAQ', 'GLE1', 'GJB3', 'GIN1', 'GDPGP1', 'GDI1', 'GDF15', 'GDAP2', 'GCAT', 'GBP1P1', 'GATAD2A', 'BICDL1', 'BLCAP', 'BRPF3', 'FBRS', 'CAP1', 'FAH', 'ETF1', 'ESRP2', 'MT-ATP8', 'EPPK1', 'EPHX1', 'ENTR1', 'ENOX2', 'ENO2', 'ENKD1', 'CACNB2', 'CACNG4', 'ELP3', 'ELOB', 'ELOA', 'CALHM2', 'EIF5', 'EIF4G2', 'EXOC7', 'CACHD1', 'FASTKD5', 'CA9', 'FARP1', 'FAM83A', 'FAM50A', 'FAM189B', 'FAM177A1', 'BTBD7P1', 'C16orf91', 'FAM13B', 'C1orf53', 'C2orf49', 'C6orf62', 'C7orf50', 'FAM126B', 'FAM111B', 'FAM104A', 'FAM102A', 'C9orf78', 'MNS1', 'AAMP', 'SYT14', 'RAB27A', 'TSHZ2', 'RAB3GAP1', 'SNRNP70', 'RAB35', 'RAB30', 'TBC1D9', 'RAB2B', 'RAB1B', 'TRAF3IP2', 'RAB12', 'RAB11FIP4', 'TSPYL1', 'PYGO2', 'TSR1', 'SNX22', 'PTP4A2', 'RAB5C', 'RABEP1', 'TRIM52-AS1', 'RAD23A', 'RNF122', 'RHOT2', 'RHBDD2', 'RGS10', 'RFK', 'TRAK1', 'RCC1L', 'TRAK2', 'RBSN', 'RBBP6', 'TBKBP1', 'RAPGEF3', 'TBCA', 'TRIM37', 'RAI14', 'SNX24', 'PTGR1', 'PSMG1', 'SPG21', 'PRR12', 'SPN', 'PRMT6', 'PREX1', 'PRDX1', 'TARS1', 'TAOK3', 'PPTC7', 'PPP4R2', 'SPRY1', 'PPP1R12B', 'TAF9B', 'PPM1G', 'PPIL1', 'PPIG', 'TXNRD2', 'TXN', 'TTL', 'PRR34-AS1', 'PSME4', 'SNX27', 'PSMD5', 'PSMD2', 'PSMD14', 'PSIP1', 'SOCS2', 'PRXL2C', 'TUBB6', 'SOS1', 'SPATS2L', 'PRRC2C', 'PRRC2A', 'PRR5L', 'TATDN2', 'RNF146', 'RNF25', 'TCHP', 'SCYL2', 'SETD2', 'SERINC5', 'SERF2', 'SENP6', 'THRB', 'TIAM1', 'SECISBP2L', 'TIMELESS', 'TPX2', 'SAT2', 'SART1', 'SMC6', 'SAMD4A', 'TMEM238', 'TMEM256', 'S100P', 'TCF7L1', 'SETD3', 'SF3B4', 'SH3RF1', 'TEDC2-AS1', 'SLC48A1', 'SMARCB1', 'TGDS', 'TGFB3', 'SLC2A6', 'THAP1', 'SLC25A24', 'SMC5', 'SLC13A5', 'SLAIN2', 'SINHCAFP3', 'SIGMAR1', 'SHOX', 'SHISA5', 'TMEM259', 'SMIM27', 'RTL8C', 'SNHG18', 'RPLP1', 'RPLP0P2', 'TNNT1', 'TOLLIP', 'SNHG9', 'TCF20', 'RPL36', 'SNORD3B-1', 'RPL35', 'TPBG', 'TPM1', 'RPL17', 'RPL12', 'TPM4', 'TCEAL9', 'RPLP2', 'RPS14', 'RSRC2', 'TNIP2', 'RRS1', 'RRP1B', 'RRAS', 'TMEM70', 'TMEM80', 'SMKR1', 'RPS5', 'TMSB4XP4', 'RPS29', 'RPS28', 'RPS21', 'TNFRSF12A', 'RPS19', 'TNFSF13B', 'MT-ND2', 'SRA1', 'SRCAP', 'POLR3GL', 'NDUFB4', 'NFIC', 'NEUROD2', 'NEDD9', 'ZNF263', 'NEDD1', 'ZNF318', 'NDUFC1', 'ZNF326', 'UBE2Q2', 'NDUFA8', 'ZNF33B', 'NCOA5', 'NCOA1', 'NCLN', 'NCKAP1', 'NCK1', 'NINJ1', 'NLK', 'NMD3', 'NME1-NME2', 'NUP93', 'ZBTB7A', 'ZFC3H1', 'NSD1', 'TAF13', 'ZFP36', 'ZHX1', 'NRG4', 'ZMIZ1', 'NQO1', 'NPLOC4', 'NOP10', 'NOM1', 'ZNF202', 'NOL4L', 'NCDN', 'NCBP3', 'NCAM1', 'MT1X', 'MT-TY', 'MT-TV', 'MT-TS2', 'MT-TP', 'MT-TN', 'ZNF702P', 'SYNJ2', 'MT-TM', 'MT-TL1', 'MT-TE', 'ZNF703', 'MT-TD', 'ZNF764', 'MT-ND5', 'ZNRF1', 'MT1E', 'MTA2', 'NBEAL2', 'ZNF480', 'NAXD', 'NACC1', 'NACA4P', 'NAA10', 'MYO5C', 'ZNF354A', 'MYO10', 'MYH14', 'ZNF418', 'MXRA5', 'MXI1', 'SULF2', 'MTND2P28', 'SYNE2', 'MTND1P23', 'NUPR2', 'OPTN', 'OTUD7B', 'PLCD3', 'PLCB4', 'PLBD2', 'USP35', 'UTP18', 'PLAU', 'UTP3', 'STARD10', 'PKIB', 'PITX1', 'PITPNA', 'PICALM', 'PI4KB', 'PHRF1', 'PHLDA2', 'PHF20L1', 'USP32', 'PLCE1', 'PHACTR1', 'PLD1', 'SREK1IP1P1', 'SRFBP1', 'SRM', 'UIMC1', 'POLE4', 'POLDIP2', 'UPK1B', 'POLB', 'TAF15', 'UQCC2', 'PMEPA1', 'SSX2IP', 'UQCR11', 'UQCRQ', 'PLIN2', 'STRBP', 'VEGFB', 'OVOL1', 'PCYT1A', 'YTHDF1', 'PCDHGA10', 'PCDHB1', 'YWHAB', 'ZBED2', 'ZBED4', 'PATL1', 'PAQR8', 'PAQR7', 'PAPOLA', 'PAK2', 'PACS1', 'ZBTB2', 'P4HA2', 'ZBTB34', 'YKT6', 'PDAP1', 'PFDN4', 'PDCD4', 'VIT', 'PGAM5', 'VMP1', 'PGAM1', 'VPS45', 'VPS9D1-AS1', 'VRK3', 'SLC9A3R1', 'WDR77', 'PDS5A', 'WSB2', 'PDLIM1', 'STRIP1', 'WWC3', 'XBP1']

979 gene(s) selected 1+ times:
['PGK1', 'MT-CYB', 'MT-CO3', 'MT-CO2', 'TMSB10', 'KRT19', 'LDHA', 'MT-ATP6', 'GAPDH', 'DDIT4', 'BNIP3L', 'HMGA1', 'P4HA1', 'BNIP3', 'NDRG1', 'FGF23', 'DSP', 'S100A11', 'S100A10', 'FAM162A', 'AKR1C2', 'MT-TQ', 'H2AC12', 'KCNJ3', 'FUT11', 'CAV1', 'BAP1', 'TRIM44', 'CACNA1A', 'WDR43', 'ADM', 'NPM1P40', 'LGALS1', 'PFKFB3', 'EGLN3', 'GPI', 'EMP2', 'ZNF302', 'HES1', 'NCALD', 'GPM6A', 'BTBD9', 'ZC3H15', 'ALDOA', 'PRRG3', 'CAMK2N1', 'BCYRN1', 'GATA3', 'RPSAP48', 'GOLGA4', 'PROSER1', 'BMPR1B', 'FOSL2', 'CAMSAP2', 'RPS6KA6', 'FEM1A', 'RGPD4-AS1', 'RHOD', 'RPL13', 'TPI1', 'TPD52L1', 'BTN3A2', 'RPS27', 'ENO1', 'C4orf3', 'TMEM64', 'H19', 'HSPH1', 'H2AC11', 'NEDD4L', 'MT-ND1', 'ZNF688', 'MT-ND4L', 'MT-ND6', 'MT-RNR1', 'MT-RNR2', 'MT-TA', 'AKR1C1', 'AKT1S1', 'ALDOC', 'MT-TS1', 'ATXN2L', 'YTHDF3', 'MAFF', 'APOOL', 'KCNJ2', 'PLOD2', 'HSP90AA1', 'RPL39', 'ARMC6', 'SLC6A8', 'IGFBP3', 'PKM', 'SLC25A48', 'KMT2D', 'SLC2A1', 'RPL37A', 'CNNM2', 'SOX4', 'SET', 'CSTB', 'CLDN4', 'CKS2', 'RPL41', 'EIF3J', 'ERO1A', 'RPL34', 'HSPA5', 'MT-ND3', 'MT-ND4', 'MT2A', 'MARCKS', 'NCL', 'NEAT1', 'KRT4', 'KPNA2', 'PARD6B', 'KDM3A', 'HSPD1', 'PLEC', 'RPL30', 'HSP90AB1', 'HEPACAM', 'H4C3', 'STMN1', 'GYS1', 'PSMA7', 'FOSL1', 'RPL15', 'RPL23', 'RPL27A', 'RPL28', 'STC2', 'MT-CO1', 'ANGPTL4', 'TMEM258', 'YWHAZ', 'UBA52', 'C19orf53', 'ATP5F1E', 'APEH', 'TOB1', 'TFF3', 'AMOTL2', 'CBX3', 'ZBTB20', 'AHNAK2', 'AURKA', 'TWNK', 'ACTB', 'B4GALT1', 'CALM2', 'TFF1', 'CAST', 'TUBA1B', 'IVL', 'IWS1', 'ARIH1', 'IRF2BPL', 'ITPK1', 'ISOC2', 'ISCU', 'IRAK1', 'INSIG1', 'INPP4B', 'JAKMIP3', 'ARID1B', 'JUN', 'KIF5B', 'KLLN', 'KLHL8', 'KLC2', 'KLC1', 'KITLG', 'KIRREL1', 'ARHGEF26', 'KIF14', 'JUND', 'KHSRP', 'KEAP1', 'ARHGEF7', 'KCTD11', 'KCNQ1OT1', 'ARID5B', 'KAT7', 'INHBA', 'CDKN1A', 'ING2', 'HMGB2', 'HIF3A', 'HEY1', 'HES4', 'ATRX', 'HELQ', 'HCFC1', 'H4C5', 'ATXN1L', 'H3C2', 'H2BC9', 'H2BC4', 'H2AC20', 'H2AC16', 'AXL', 'BAZ2A', 'GSE1', 'GRK2', 'HILPDA', 'HNRNPA2B1', 'INF2', 'HOXC13', 'INCENP', 'ILRUN', 'IGFBP5', 'ARHGAP42', 'ARL13B', 'ARL2', 'IFITM3', 'IFI27L2', 'ARNTL', 'ARNTL2', 'HSPA8', 'ARPP19', 'HSP90B1', 'ARSA', 'ARTN', 'ATF5', 'ATN1', 'ARHGDIA', 'ARF3', 'ARHGAP26', 'MARK3', 'MLLT6', 'MLLT3', 'MKNK1', 'MIXL1', 'MIR663AHG', 'MIR210HG', 'MIOS-DT', 'MIF', 'MGRN1', 'MGLL', 'METTL26', 'MELTF-AS1', 'MED18', 'MDM2', 'MCM4', 'MCM3AP', 'MB', 'MMP1', 'AMFR', 'AKR1C3', 'MSMB', 'ABL1', 'ABO', 'ACAT2', 'ACSL4', 'MSR1', 'ADARB1', 'AFF1', 'AJAP1', 'MMP2', 'AK4', 'AKAP5', 'MRPL55', 'MPHOSPH9', 'MPHOSPH6', 'MPDU1', 'ZRANB1', 'MAZ', 'MARK2', 'KPNA4', 'MAPKAPK2', 'LINC01291', 'LINC01133', 'LINC01116', 'LIMCH1', 'ANKRD9', 'LETM1', 'LDLRAP1', 'ANXA6', 'LDHB', 'LCLAT1', 'LAD1', 'KYNU', 'KRT80', 'KRT8', 'BBOF1', 'ARFGEF1', 'KRT18', 'LINC01304', 'LINC01902', 'LINC02367', 'LXN', 'MAP3K13', 'MAP2K3', 'MALAT1', 'ANKEF1', 'MAD2L1', 'LYAR', 'ANKRD17', 'LTBR', 'LINC02511', 'LRRFIP2', 'LPP', 'ANKRD40', 'LOXL2', 'LMNB2', 'ANKRD52', 'LINC02541', 'GREM1', 'BCL3', 'GPRC5A', 'DNAJA3', 'DNAH11', 'DNAAF5', 'DLD', 'DKK1', 'DKC1', 'DHX38', 'DHX37', 'CAVIN3', 'DHCR7', 'DGKZ', 'DGKD', 'DERA', 'DDX54', 'CBFA2T3', 'DDX23', 'DDIT3', 'DCTN1', 'DNAJA1', 'DNAJA4', 'DBT', 'DNAJC21', 'CAPZA1', 'CARM1', 'CASP8AP2', 'EGLN1', 'EFNA5', 'EFNA2', 'ECH1', 'EBAG9', 'DYSF', 'DYNC2I2', 'DVL3', 'DUSP9', 'DUSP5', 'DTYMK', 'DTNB', 'DSCAM-AS1', 'DNMT3A', 'DCAKD', 'DBNDD1', 'EIF2B4', 'CNOT6L', 'CMIP', 'CLTB', 'CLSPN', 'CLIP2', 'CLIC1', 'CCNG2', 'CD44', 'CITED2', 'CD47', 'CDC20', 'CIAO2A', 'CHAC2', 'CFAP97', 'CFAP251', 'CERS2', 'CEP83', 'CEP120', 'CCDC34', 'CNOT9', 'DANT1', 'CNR2', 'DAAM1', 'CYP1B1', 'CTXN1', 'CCDC168', 'CSNK2A2', 'CSK', 'CS', 'CRTC2', 'CRNDE', 'CRIP2', 'CREB1', 'CCDC18', 'CPTP', 'CPNE2', 'CPEB1', 'COX8A', 'COL6A3', 'EHBP1L1', 'EIF3A', 'BCAS3', 'GAB2', 'BLOC1S3', 'FTL', 'FSD1L', 'FRY', 'FRS2', 'BMS1', 'BOLA3', 'FOS', 'FLOT2', 'FLNA', 'FGF8', 'BRAT1', 'BRIP1', 'FDFT1', 'BRMS1', 'FBXL18', 'FBXL17', 'FYN', 'GABPB2', 'FBXL16', 'GABRE', 'GPATCH4', 'CENPB', 'BHLHE40', 'GOLGA3', 'GNAQ', 'GLE1', 'GJB3', 'GIN1', 'GDPGP1', 'GDI1', 'GDF15', 'GDAP2', 'GCAT', 'GBP1P1', 'GATAD2A', 'BICDL1', 'BLCAP', 'BRPF3', 'FBRS', 'CAP1', 'FAH', 'ETF1', 'ESRP2', 'MT-ATP8', 'EPPK1', 'EPHX1', 'ENTR1', 'ENOX2', 'ENO2', 'ENKD1', 'CACNB2', 'CACNG4', 'ELP3', 'ELOB', 'ELOA', 'CALHM2', 'EIF5', 'EIF4G2', 'EXOC7', 'CACHD1', 'FASTKD5', 'CA9', 'FARP1', 'FAM83A', 'FAM50A', 'FAM189B', 'FAM177A1', 'BTBD7P1', 'C16orf91', 'FAM13B', 'C1orf53', 'C2orf49', 'C6orf62', 'C7orf50', 'FAM126B', 'FAM111B', 'FAM104A', 'FAM102A', 'C9orf78', 'MNS1', 'AAMP', 'SYT14', 'RAB27A', 'TSHZ2', 'RAB3GAP1', 'SNRNP70', 'RAB35', 'RAB30', 'TBC1D9', 'RAB2B', 'RAB1B', 'TRAF3IP2', 'RAB12', 'RAB11FIP4', 'TSPYL1', 'PYGO2', 'TSR1', 'SNX22', 'PTP4A2', 'RAB5C', 'RABEP1', 'TRIM52-AS1', 'RAD23A', 'RNF122', 'RHOT2', 'RHBDD2', 'RGS10', 'RFK', 'TRAK1', 'RCC1L', 'TRAK2', 'RBSN', 'RBBP6', 'TBKBP1', 'RAPGEF3', 'TBCA', 'TRIM37', 'RAI14', 'SNX24', 'PTGR1', 'PSMG1', 'SPG21', 'PRR12', 'SPN', 'PRMT6', 'PREX1', 'PRDX1', 'TARS1', 'TAOK3', 'PPTC7', 'PPP4R2', 'SPRY1', 'PPP1R12B', 'TAF9B', 'PPM1G', 'PPIL1', 'PPIG', 'TXNRD2', 'TXN', 'TTL', 'PRR34-AS1', 'PSME4', 'SNX27', 'PSMD5', 'PSMD2', 'PSMD14', 'PSIP1', 'SOCS2', 'PRXL2C', 'TUBB6', 'SOS1', 'SPATS2L', 'PRRC2C', 'PRRC2A', 'PRR5L', 'TATDN2', 'RNF146', 'RNF25', 'TCHP', 'SCYL2', 'SETD2', 'SERINC5', 'SERF2', 'SENP6', 'THRB', 'TIAM1', 'SECISBP2L', 'TIMELESS', 'TPX2', 'SAT2', 'SART1', 'SMC6', 'SAMD4A', 'TMEM238', 'TMEM256', 'S100P', 'TCF7L1', 'SETD3', 'SF3B4', 'SH3RF1', 'TEDC2-AS1', 'SLC48A1', 'SMARCB1', 'TGDS', 'TGFB3', 'SLC2A6', 'THAP1', 'SLC25A24', 'SMC5', 'SLC13A5', 'SLAIN2', 'SINHCAFP3', 'SIGMAR1', 'SHOX', 'SHISA5', 'TMEM259', 'SMIM27', 'RTL8C', 'SNHG18', 'RPLP1', 'RPLP0P2', 'TNNT1', 'TOLLIP', 'SNHG9', 'TCF20', 'RPL36', 'SNORD3B-1', 'RPL35', 'TPBG', 'TPM1', 'RPL17', 'RPL12', 'TPM4', 'TCEAL9', 'RPLP2', 'RPS14', 'RSRC2', 'TNIP2', 'RRS1', 'RRP1B', 'RRAS', 'TMEM70', 'TMEM80', 'SMKR1', 'RPS5', 'TMSB4XP4', 'RPS29', 'RPS28', 'RPS21', 'TNFRSF12A', 'RPS19', 'TNFSF13B', 'MT-ND2', 'SRA1', 'SRCAP', 'POLR3GL', 'NDUFB4', 'NFIC', 'NEUROD2', 'NEDD9', 'ZNF263', 'NEDD1', 'ZNF318', 'NDUFC1', 'ZNF326', 'UBE2Q2', 'NDUFA8', 'ZNF33B', 'NCOA5', 'NCOA1', 'NCLN', 'NCKAP1', 'NCK1', 'NINJ1', 'NLK', 'NMD3', 'NME1-NME2', 'NUP93', 'ZBTB7A', 'ZFC3H1', 'NSD1', 'TAF13', 'ZFP36', 'ZHX1', 'NRG4', 'ZMIZ1', 'NQO1', 'NPLOC4', 'NOP10', 'NOM1', 'ZNF202', 'NOL4L', 'NCDN', 'NCBP3', 'NCAM1', 'MT1X', 'MT-TY', 'MT-TV', 'MT-TS2', 'MT-TP', 'MT-TN', 'ZNF702P', 'SYNJ2', 'MT-TM', 'MT-TL1', 'MT-TE', 'ZNF703', 'MT-TD', 'ZNF764', 'MT-ND5', 'ZNRF1', 'MT1E', 'MTA2', 'NBEAL2', 'ZNF480', 'NAXD', 'NACC1', 'NACA4P', 'NAA10', 'MYO5C', 'ZNF354A', 'MYO10', 'MYH14', 'ZNF418', 'MXRA5', 'MXI1', 'SULF2', 'MTND2P28', 'SYNE2', 'MTND1P23', 'NUPR2', 'OPTN', 'OTUD7B', 'PLCD3', 'PLCB4', 'PLBD2', 'USP35', 'UTP18', 'PLAU', 'UTP3', 'STARD10', 'PKIB', 'PITX1', 'PITPNA', 'PICALM', 'PI4KB', 'PHRF1', 'PHLDA2', 'PHF20L1', 'USP32', 'PLCE1', 'PHACTR1', 'PLD1', 'SREK1IP1P1', 'SRFBP1', 'SRM', 'UIMC1', 'POLE4', 'POLDIP2', 'UPK1B', 'POLB', 'TAF15', 'UQCC2', 'PMEPA1', 'SSX2IP', 'UQCR11', 'UQCRQ', 'PLIN2', 'STRBP', 'VEGFB', 'OVOL1', 'PCYT1A', 'YTHDF1', 'PCDHGA10', 'PCDHB1', 'YWHAB', 'ZBED2', 'ZBED4', 'PATL1', 'PAQR8', 'PAQR7', 'PAPOLA', 'PAK2', 'PACS1', 'ZBTB2', 'P4HA2', 'ZBTB34', 'YKT6', 'PDAP1', 'PFDN4', 'PDCD4', 'VIT', 'PGAM5', 'VMP1', 'PGAM1', 'VPS45', 'VPS9D1-AS1', 'VRK3', 'SLC9A3R1', 'WDR77', 'PDS5A', 'WSB2', 'PDLIM1', 'STRIP1', 'WWC3', 'XBP1', 'TFRC', 'CDC6', 'ATP9A', 'CD9', 'ATP5MK', 'ATP5MG', 'CDC25B', 'ATP5ME', 'UBC', 'UGDH', 'ATAD2', 'ASB2', 'CDK2AP2', 'TYSND1', 'ARRDC3', 'ARPC1B', 'CEACAM5', 'VCPIP1', 'VEGFA', 'WTAPP1', 'ALDH1A3', 'ZNF473', 'ADAP1', 'ACLY', 'UBB', 'TXNIP', 'CANX', 'TRIM29', 'THBS1', 'CALB1', 'TMEM45A', 'TMSB4X', 'C1orf116', 'C10orf55', 'BUB1B', 'BUB1', 'TPT1', 'TRIM16', 'TSPO', 'TUBG1', 'BIRC5', 'BEND7', 'TST', 'TUBB', 'CCM2', 'TUBB4B', 'CENPF', 'CCNB1', 'CCNB2', 'BAG3', 'TUBD1', 'RPS15A', 'SYTL2', 'ID3', 'HNRNPM', 'HNRNPU', 'HPCAL1', 'HRH1', 'PLK2', 'HSPB1', 'IER2', 'HLA-A', 'IFITM2', 'PLAC8', 'PHC1', 'IRF2BP2', 'IRF6', 'ISG15', 'HMGCS1', 'HK2', 'PFKP', 'H2AX', 'GPX2', 'GSTP1', 'H1-0', 'H1-1', 'H1-3', 'PRNP', 'PRC1', 'POLR2A', 'PPP1R3G', 'HBP1', 'HERPUD1', 'PPIF', 'POLR3A', 'POLR2L', 'ITGA6', 'PFKFB4', 'PRSS8', 'MUL1', 'LMNA', 'NECTIN2', 'NDUFB2', 'LY6D', 'MCM7', 'MYC', 'MIF-AS1', 'NR4A1', 'MTATP6P1', 'MOB3A', 'MOV10', 'MRNIP', 'MRPS2', 'MSMO1', 'NOLC1', 'LBH', 'PERP', 'PCDH1', 'JUNB', 'JUP', 'PDK1', 'PDIA3', 'PCNA', 'KDM5B', 'KIF23', 'LAMB3', 'KIF2C', 'PABPC1', 'KNSTRN', 'NUP188', 'NT5C', 'NRP1', 'PRSS23', 'PSAP', 'CHAC1', 'DHRS3', 'DCBLD2', 'SLCO4A1', 'DDX5', 'SLC3A2', 'SLC39A6', 'SLC38A2', 'SLC20A1', 'CYB561A3', 'DNMT1', 'SENP3', 'SEMA4B', 'SCD', 'EBP', 'EEF1A1', 'CYP1B1-AS1', 'CXCL1', 'S100A6', 'SPP1', 'CKAP2', 'SRXN1', 'CLDN7', 'SRSF8', 'SQSTM1', 'SQLE', 'COX7A2', 'SNRNP25', 'COX7C', 'CPEB4', 'SPAG5', 'CRABP2', 'SNX33', 'SNRPD2', 'EEF2', 'S100A2', 'PSME2', 'FSCN1', 'FDPS', 'FGD5-AS1', 'FGFBP1', 'RALGDS', 'FN1', 'RAC1', 'FTH1', 'ROMO1', 'FYB1', 'QSOX1', 'PYCR3', 'PUSL1', 'PTMS', 'GFRA1', 'FASN', 'RPL10', 'EIF4A2', 'RPS12', 'RPSA', 'RPS8', 'RPS3', 'RPS2', 'RPS16', 'RPS15', 'EZR', 'RPL11', 'F3', 'RPL8', 'RPL37', 'RPL35A', 'FAM13A', 'RPL21', 'ZWINT']

ss_mcf7_top_pcs_occurrences = count_and_sort_occurrences([
    ss_mcf7_pca_logit_pcs,
    ss_mcf7_pca_svm_pcs,
    ss_mcf7_pca_random_forest_pcs
], False)
ss_mcf7_top_pcs = {}

for i in range(3, 0, -1):
    ss_mcf7_top_pcs[i] = ss_mcf7_top_pcs.get(i + 1, []) + filter_by_occurrences(ss_mcf7_top_pcs_occurrences, i)

for i in range(3, 0, -1):
    if len(ss_mcf7_top_pcs[i]) == 0:
        continue
    print(f"{len(ss_mcf7_top_pcs[i])} PC(s) selected for SmartSeq MCF7 across {i}+ model(s):")
    print(ss_mcf7_top_pcs[i])
    print()

3 PC(s) selected for SmartSeq MCF7 across 3+ model(s):
[6, 3, 1]

8 PC(s) selected for SmartSeq MCF7 across 2+ model(s):
[6, 3, 1, 18, 17, 16, 12, 8]

13 PC(s) selected for SmartSeq MCF7 across 1+ model(s):
[6, 3, 1, 18, 17, 16, 12, 8, 15, 9, 5, 4, 2]

ss_hcc_top_pcs_occurrences = count_and_sort_occurrences([
    ss_hcc_pca_logit_pcs,
    ss_hcc_pca_svm_pcs,
    ss_hcc_pca_random_forest_pcs
], False)
ss_hcc_top_pcs = {}

for i in range(3, 0, -1):
    ss_hcc_top_pcs[i] = ss_hcc_top_pcs.get(i + 1, []) + filter_by_occurrences(ss_hcc_top_pcs_occurrences, i)

for i in range(3, 0, -1):
    if len(ss_hcc_top_pcs[i]) == 0:
        continue
    print(f"{len(ss_hcc_top_pcs[i])} PC(s) selected for SmartSeq HCC across {i}+ models(s):")
    print(ss_hcc_top_pcs[i])
    print()

2 PC(s) selected for SmartSeq HCC across 3+ models(s):
[3, 2]

7 PC(s) selected for SmartSeq HCC across 2+ models(s):
[3, 2, 26, 17, 12, 10, 9]

15 PC(s) selected for SmartSeq HCC across 1+ models(s):
[3, 2, 26, 17, 12, 10, 9, 32, 30, 23, 21, 16, 15, 13, 4]

ds_mcf7_top_pcs_occurrences = count_and_sort_occurrences([
    ds_mcf7_pca_logit_pcs,
    ds_mcf7_pca_svm_pcs,
    ds_mcf7_pca_random_forest_pcs
], False)
ds_mcf7_top_pcs = {}

for i in range(3, 0, -1):
    ds_mcf7_top_pcs[i] = ds_mcf7_top_pcs.get(i + 1, []) + filter_by_occurrences(ds_mcf7_top_pcs_occurrences, i)

for i in range(3, 0, -1):
    if len(ds_mcf7_top_pcs[i]) == 0:
        continue
    print(f"{len(ds_mcf7_top_pcs[i])} PC(s) selected for DropSeq MCF7 across {i}+ models(s):")
    print(ds_mcf7_top_pcs[i])
    print()

42 PC(s) selected for DropSeq MCF7 across 3+ models(s):
[1, 149, 82, 95, 110, 116, 121, 140, 142, 145, 147, 157, 55, 170, 232, 442, 317, 318, 322, 344, 380, 383, 60, 167, 26, 16, 36, 25, 32, 8, 15, 33, 17, 6, 37, 27, 28, 3, 18, 2, 19, 5]

278 PC(s) selected for DropSeq MCF7 across 2+ models(s):
[1, 149, 82, 95, 110, 116, 121, 140, 142, 145, 147, 157, 55, 170, 232, 442, 317, 318, 322, 344, 380, 383, 60, 167, 26, 16, 36, 25, 32, 8, 15, 33, 17, 6, 37, 27, 28, 3, 18, 2, 19, 5, 267, 43, 271, 273, 275, 264, 252, 263, 254, 281, 249, 247, 239, 236, 235, 234, 231, 230, 219, 218, 213, 212, 279, 302, 282, 286, 385, 381, 377, 375, 371, 370, 364, 361, 353, 352, 350, 348, 342, 341, 339, 756, 332, 327, 323, 320, 319, 312, 305, 206, 293, 291, 287, 211, 200, 205, 31, 114, 112, 107, 105, 104, 100, 99, 94, 92, 91, 88, 87, 85, 81, 74, 71, 69, 66, 65, 62, 61, 57, 56, 40, 52, 46, 45, 115, 118, 204, 119, 203, 201, 44, 198, 195, 193, 191, 190, 188, 177, 175, 173, 172, 389, 161, 160, 153, 146, 141, 29, 138, 135, 133, 128, 127, 30, 120, 387, 758, 391, 543, 552, 555, 556, 557, 564, 565, 576, 580, 582, 585, 591, 596, 597, 598, 599, 602, 606, 546, 541, 486, 540, 491, 494, 495, 496, 497, 499, 504, 507, 508, 510, 512, 517, 520, 522, 392, 534, 538, 610, 612, 615, 621, 696, 698, 700, 702, 718, 722, 724, 726, 732, 733, 734, 741, 742, 746, 751, 754, 755, 682, 681, 677, 647, 623, 626, 631, 632, 633, 642, 646, 650, 675, 652, 653, 655, 658, 661, 672, 674, 487, 527, 485, 411, 406, 466, 464, 462, 461, 460, 429, 459, 409, 455, 449, 403, 415, 438, 418, 419, 436, 435, 434, 433, 484, 431, 469, 408, 402, 393, 475, 481, 400, 399, 401, 398, 471, 470]

372 PC(s) selected for DropSeq MCF7 across 1+ models(s):
[1, 149, 82, 95, 110, 116, 121, 140, 142, 145, 147, 157, 55, 170, 232, 442, 317, 318, 322, 344, 380, 383, 60, 167, 26, 16, 36, 25, 32, 8, 15, 33, 17, 6, 37, 27, 28, 3, 18, 2, 19, 5, 267, 43, 271, 273, 275, 264, 252, 263, 254, 281, 249, 247, 239, 236, 235, 234, 231, 230, 219, 218, 213, 212, 279, 302, 282, 286, 385, 381, 377, 375, 371, 370, 364, 361, 353, 352, 350, 348, 342, 341, 339, 756, 332, 327, 323, 320, 319, 312, 305, 206, 293, 291, 287, 211, 200, 205, 31, 114, 112, 107, 105, 104, 100, 99, 94, 92, 91, 88, 87, 85, 81, 74, 71, 69, 66, 65, 62, 61, 57, 56, 40, 52, 46, 45, 115, 118, 204, 119, 203, 201, 44, 198, 195, 193, 191, 190, 188, 177, 175, 173, 172, 389, 161, 160, 153, 146, 141, 29, 138, 135, 133, 128, 127, 30, 120, 387, 758, 391, 543, 552, 555, 556, 557, 564, 565, 576, 580, 582, 585, 591, 596, 597, 598, 599, 602, 606, 546, 541, 486, 540, 491, 494, 495, 496, 497, 499, 504, 507, 508, 510, 512, 517, 520, 522, 392, 534, 538, 610, 612, 615, 621, 696, 698, 700, 702, 718, 722, 724, 726, 732, 733, 734, 741, 742, 746, 751, 754, 755, 682, 681, 677, 647, 623, 626, 631, 632, 633, 642, 646, 650, 675, 652, 653, 655, 658, 661, 672, 674, 487, 527, 485, 411, 406, 466, 464, 462, 461, 460, 429, 459, 409, 455, 449, 403, 415, 438, 418, 419, 436, 435, 434, 433, 484, 431, 469, 408, 402, 393, 475, 481, 400, 399, 401, 398, 471, 470, 424, 9, 745, 54, 337, 7, 427, 666, 420, 59, 4, 47, 422, 426, 667, 48, 13, 12, 729, 23, 367, 21, 365, 20, 376, 736, 379, 711, 705, 35, 362, 743, 38, 14, 358, 355, 687, 685, 335, 97, 331, 515, 467, 269, 265, 181, 182, 186, 260, 519, 518, 197, 258, 514, 329, 257, 255, 474, 253, 506, 483, 245, 221, 243, 241, 240, 278, 151, 458, 571, 644, 328, 430, 326, 627, 96, 624, 321, 437, 314, 603, 307, 446, 303, 594, 592, 301, 300, 297, 579, 577, 144, 457, 338]

ds_hcc_top_pcs_occurrences = count_and_sort_occurrences([
    ds_hcc_pca_logit_pcs,
    ds_hcc_pca_svm_pcs,
    ds_hcc_pca_random_forest_pcs,
], False)
ds_hcc_top_pcs = {}

for i in range(3, 0, -1):
    ds_hcc_top_pcs[i] = ds_hcc_top_pcs.get(i + 1, []) + filter_by_occurrences(ds_hcc_top_pcs_occurrences, i)

for i in range(3, 0, -1):
    if len(ds_hcc_top_pcs[i]) == 0:
        continue
    print(f"{len(ds_hcc_top_pcs[i])} PC(s) selected for DropSeq HCC across {i}+ models(s):")
    print(ds_hcc_top_pcs[i])
    print()

69 PC(s) selected for DropSeq HCC across 3+ models(s):
[187, 190, 201, 301, 198, 197, 99, 26, 24, 23, 21, 30, 189, 20, 19, 18, 102, 184, 182, 63, 208, 31, 176, 47, 259, 41, 39, 46, 38, 240, 262, 37, 48, 290, 90, 49, 270, 53, 36, 54, 34, 76, 15, 45, 11, 141, 147, 127, 8, 69, 155, 6, 142, 106, 5, 103, 161, 145, 65, 167, 12, 4, 169, 170, 172, 3, 2, 72, 174]

298 PC(s) selected for DropSeq HCC across 2+ models(s):
[187, 190, 201, 301, 198, 197, 99, 26, 24, 23, 21, 30, 189, 20, 19, 18, 102, 184, 182, 63, 208, 31, 176, 47, 259, 41, 39, 46, 38, 240, 262, 37, 48, 290, 90, 49, 270, 53, 36, 54, 34, 76, 15, 45, 11, 141, 147, 127, 8, 69, 155, 6, 142, 106, 5, 103, 161, 145, 65, 167, 12, 4, 169, 170, 172, 3, 2, 72, 174, 77, 351, 350, 356, 266, 319, 265, 357, 358, 263, 345, 261, 359, 260, 360, 80, 272, 294, 275, 282, 315, 313, 312, 325, 310, 255, 326, 329, 308, 842, 332, 334, 303, 336, 300, 297, 295, 318, 292, 339, 341, 843, 219, 254, 153, 183, 181, 180, 179, 178, 177, 175, 171, 166, 162, 157, 154, 148, 193, 143, 140, 139, 137, 136, 135, 115, 117, 131, 126, 118, 124, 191, 96, 88, 227, 249, 247, 89, 243, 239, 238, 237, 235, 234, 231, 230, 229, 225, 200, 224, 221, 220, 369, 218, 217, 215, 213, 92, 210, 94, 205, 362, 391, 371, 672, 640, 641, 642, 27, 651, 657, 691, 577, 692, 693, 698, 700, 701, 16, 637, 29, 630, 622, 613, 612, 610, 609, 603, 32, 600, 598, 594, 372, 584, 583, 582, 713, 717, 718, 785, 835, 831, 7, 814, 813, 810, 809, 808, 807, 799, 794, 793, 792, 789, 784, 730, 783, 781, 10, 777, 769, 766, 763, 761, 760, 758, 756, 749, 747, 746, 581, 586, 576, 413, 450, 448, 575, 447, 445, 444, 55, 439, 438, 60, 427, 420, 415, 414, 412, 453, 409, 408, 401, 399, 396, 120, 384, 383, 380, 379, 377, 375, 374, 373, 451, 123, 509, 563, 457, 562, 525, 551, 521, 516, 515, 540, 550, 507, 561, 503, 564, 566, 494, 490, 567, 479, 474, 461, 528]

400 PC(s) selected for DropSeq HCC across 1+ models(s):
[187, 190, 201, 301, 198, 197, 99, 26, 24, 23, 21, 30, 189, 20, 19, 18, 102, 184, 182, 63, 208, 31, 176, 47, 259, 41, 39, 46, 38, 240, 262, 37, 48, 290, 90, 49, 270, 53, 36, 54, 34, 76, 15, 45, 11, 141, 147, 127, 8, 69, 155, 6, 142, 106, 5, 103, 161, 145, 65, 167, 12, 4, 169, 170, 172, 3, 2, 72, 174, 77, 351, 350, 356, 266, 319, 265, 357, 358, 263, 345, 261, 359, 260, 360, 80, 272, 294, 275, 282, 315, 313, 312, 325, 310, 255, 326, 329, 308, 842, 332, 334, 303, 336, 300, 297, 295, 318, 292, 339, 341, 843, 219, 254, 153, 183, 181, 180, 179, 178, 177, 175, 171, 166, 162, 157, 154, 148, 193, 143, 140, 139, 137, 136, 135, 115, 117, 131, 126, 118, 124, 191, 96, 88, 227, 249, 247, 89, 243, 239, 238, 237, 235, 234, 231, 230, 229, 225, 200, 224, 221, 220, 369, 218, 217, 215, 213, 92, 210, 94, 205, 362, 391, 371, 672, 640, 641, 642, 27, 651, 657, 691, 577, 692, 693, 698, 700, 701, 16, 637, 29, 630, 622, 613, 612, 610, 609, 603, 32, 600, 598, 594, 372, 584, 583, 582, 713, 717, 718, 785, 835, 831, 7, 814, 813, 810, 809, 808, 807, 799, 794, 793, 792, 789, 784, 730, 783, 781, 10, 777, 769, 766, 763, 761, 760, 758, 756, 749, 747, 746, 581, 586, 576, 413, 450, 448, 575, 447, 445, 444, 55, 439, 438, 60, 427, 420, 415, 414, 412, 453, 409, 408, 401, 399, 396, 120, 384, 383, 380, 379, 377, 375, 374, 373, 451, 123, 509, 563, 457, 562, 525, 551, 521, 516, 515, 540, 550, 507, 561, 503, 564, 566, 494, 490, 567, 479, 474, 461, 528, 17, 70, 9, 35, 73, 74, 42, 44, 68, 86, 110, 109, 95, 75, 97, 56, 100, 13, 78, 14, 305, 121, 510, 656, 653, 646, 644, 632, 608, 604, 601, 599, 597, 593, 565, 553, 543, 541, 539, 534, 667, 674, 685, 812, 841, 839, 832, 829, 828, 820, 815, 778, 689, 771, 755, 733, 716, 714, 705, 699, 523, 499, 125, 497, 250, 245, 233, 212, 207, 202, 199, 196, 195, 192, 185, 159, 156, 152, 151, 134, 133, 253, 257, 267, 403, 488, 466, 452, 443, 434, 429, 423, 402, 269, 393, 382, 344, 338, 323, 317, 283, 1]

top_genes_ss_mcf7 = {i: list(filter(lambda x: x in X_ss_mcf7.columns, top_genes[i])) for i in range (1, 13)}
X_top_genes_ss_mcf7 = {
    i: X_ss_mcf7.loc[:, top_genes_ss_mcf7[i]] for i in range(1, 13)
}

top_genes_ss_hcc = {i: list(filter(lambda x: x in X_ss_hcc.columns, top_genes[i])) for i in range (1, 13)}
X_top_genes_ss_hcc = {
    i: X_ss_hcc.loc[:, top_genes_ss_hcc[i]] for i in range(1, 13)
}

top_genes_ds_mcf7 = {i: list(filter(lambda x: x in X_ds_mcf7.columns, top_genes[i])) for i in range (1, 13)}
X_top_genes_ds_mcf7 = {
    i: X_ds_mcf7.loc[:, top_genes_ds_mcf7[i]] for i in range(1, 13)
}

top_genes_ds_hcc = {i: list(filter(lambda x: x in X_ds_hcc.columns, top_genes[i])) for i in range (1, 13)}
X_top_genes_ds_hcc = {
    i: X_ds_hcc.loc[:, top_genes_ds_hcc[i]] for i in range(1, 13)
}

for i, genes in top_genes_ss_mcf7.items():
    print(f"{len(genes)} genes selected {i}+ times for SmartSeq MCF7")
print()

for i, genes in top_genes_ss_hcc.items():
    print(f"{len(genes)} genes selected {i}+ times for SmartSeq HCC")
print()

for i, genes in top_genes_ds_mcf7.items():
    print(f"{len(genes)} genes selected {i}+ times for DropSeq MCF7")
print()

for i, genes in top_genes_ds_hcc.items():
    print(f"{len(genes)} genes selected {i}+ times for DropSeq HCC")
print()

302 genes selected 1+ times for SmartSeq MCF7
195 genes selected 2+ times for SmartSeq MCF7
76 genes selected 3+ times for SmartSeq MCF7
47 genes selected 4+ times for SmartSeq MCF7
30 genes selected 5+ times for SmartSeq MCF7
17 genes selected 6+ times for SmartSeq MCF7
9 genes selected 7+ times for SmartSeq MCF7
6 genes selected 8+ times for SmartSeq MCF7
3 genes selected 9+ times for SmartSeq MCF7
2 genes selected 10+ times for SmartSeq MCF7
1 genes selected 11+ times for SmartSeq MCF7
1 genes selected 12+ times for SmartSeq MCF7

279 genes selected 1+ times for SmartSeq HCC
160 genes selected 2+ times for SmartSeq HCC
63 genes selected 3+ times for SmartSeq HCC
40 genes selected 4+ times for SmartSeq HCC
26 genes selected 5+ times for SmartSeq HCC
15 genes selected 6+ times for SmartSeq HCC
8 genes selected 7+ times for SmartSeq HCC
7 genes selected 8+ times for SmartSeq HCC
3 genes selected 9+ times for SmartSeq HCC
2 genes selected 10+ times for SmartSeq HCC
1 genes selected 11+ times for SmartSeq HCC
1 genes selected 12+ times for SmartSeq HCC

554 genes selected 1+ times for DropSeq MCF7
507 genes selected 2+ times for DropSeq MCF7
112 genes selected 3+ times for DropSeq MCF7
74 genes selected 4+ times for DropSeq MCF7
31 genes selected 5+ times for DropSeq MCF7
13 genes selected 6+ times for DropSeq MCF7
8 genes selected 7+ times for DropSeq MCF7
6 genes selected 8+ times for DropSeq MCF7
3 genes selected 9+ times for DropSeq MCF7
2 genes selected 10+ times for DropSeq MCF7
1 genes selected 11+ times for DropSeq MCF7
1 genes selected 12+ times for DropSeq MCF7

578 genes selected 1+ times for DropSeq HCC
507 genes selected 2+ times for DropSeq HCC
136 genes selected 3+ times for DropSeq HCC
94 genes selected 4+ times for DropSeq HCC
44 genes selected 5+ times for DropSeq HCC
20 genes selected 6+ times for DropSeq HCC
10 genes selected 7+ times for DropSeq HCC
7 genes selected 8+ times for DropSeq HCC
3 genes selected 9+ times for DropSeq HCC
2 genes selected 10+ times for DropSeq HCC
1 genes selected 11+ times for DropSeq HCC
1 genes selected 12+ times for DropSeq HCC

ss_mcf7_top_genes_logit: dict[int, TrainedModelWrapper] = {}

ss_hcc_top_genes_logit: dict[int, TrainedModelWrapper] = {}

ds_mcf7_top_genes_logit: dict[int, TrainedModelWrapper] = {}

ds_hcc_top_genes_logit: dict[int, TrainedModelWrapper] = {}

for i in [12, 6, 1]:
    ss_mcf7_top_genes_logit[i] = train_test_logistic_regression(X_top_genes_ss_mcf7[i], y_ss_mcf7, n_jobs = -1, verbose = False)
    print(f"Genes selected across {i}+ model(s). Accuracy: {ss_mcf7_top_genes_logit[i].accuracy}")

Genes selected across 12+ model(s). Accuracy: 0.9523809523809523
Genes selected across 6+ model(s). Accuracy: 1.0
Genes selected across 1+ model(s). Accuracy: 1.0

for i in [12, 6, 1]:
    ss_hcc_top_genes_logit[i] = train_test_logistic_regression(X_top_genes_ss_hcc[i], y_ss_hcc, n_jobs = -1, verbose = False)
    print(f"Genes selected across {i}+ model(s). Accuracy: {ss_hcc_top_genes_logit[i].accuracy}")

Genes selected across 12+ model(s). Accuracy: 0.9782608695652174
Genes selected across 6+ model(s). Accuracy: 0.9782608695652174
Genes selected across 1+ model(s). Accuracy: 0.9782608695652174

for i in [12, 6, 1]:
    ds_mcf7_top_genes_logit[i] = train_test_logistic_regression(X_top_genes_ds_mcf7[i], y_ds_mcf7, n_jobs = -1, verbose = False)
    print(f"Genes selected across {i}+ model(s). Accuracy: {ds_mcf7_top_genes_logit[i].accuracy}")

Genes selected across 12+ model(s). Accuracy: 0.726465692620677
Genes selected across 6+ model(s). Accuracy: 0.8823746994636582
Genes selected across 1+ model(s). Accuracy: 0.9774366561864251

for i in [12, 6, 1]:
    ds_hcc_top_genes_logit[i] = train_test_logistic_regression(X_top_genes_ds_hcc[i], y_ds_hcc, n_jobs = -1, verbose = False)
    print(f"Genes selected across {i}+ model(s). Accuracy: {ds_hcc_top_genes_logit[i].accuracy}")

Genes selected across 12+ model(s). Accuracy: 0.6061018795968401
Genes selected across 6+ model(s). Accuracy: 0.8136747480250613
Genes selected across 1+ model(s). Accuracy: 0.9476981748842277

ss_mcf7_top_genes_svm: dict[int, TrainedModelWrapper] = {}

ss_hcc_top_genes_svm: dict[int, TrainedModelWrapper] = {}

ds_mcf7_top_genes_svm: dict[int, TrainedModelWrapper] = {}

ds_hcc_top_genes_svm: dict[int, TrainedModelWrapper] = {}

for i in [12, 6, 1]:
    ss_mcf7_top_genes_svm[i] = train_test_svm(X_top_genes_ss_mcf7[i], y_ss_mcf7, n_jobs = -1, verbose = False)
    print(f"Genes selected across {i}+ model(s). Accuracy: {ss_mcf7_top_genes_svm[i].accuracy}")

Genes selected across 12+ model(s). Accuracy: 0.9365079365079365
Genes selected across 6+ model(s). Accuracy: 1.0
Genes selected across 1+ model(s). Accuracy: 1.0

for i in [12, 6, 1]:
    ss_hcc_top_genes_svm[i] = train_test_svm(X_top_genes_ss_hcc[i], y_ss_hcc, n_jobs = -1, verbose = False)
    print(f"Genes selected across {i}+ model(s). Accuracy: {ss_hcc_top_genes_svm[i].accuracy}")

Genes selected across 12+ model(s). Accuracy: 0.9782608695652174
Genes selected across 6+ model(s). Accuracy: 0.9565217391304348
Genes selected across 1+ model(s). Accuracy: 0.9782608695652174

for i in [12, 6, 1]:
    ds_mcf7_top_genes_svm[i] = train_test_svm(X_top_genes_ds_mcf7[i], y_ds_mcf7, n_jobs = -1, verbose = False)
    print(f"Genes selected across {i}+ model(s). Accuracy: {ds_mcf7_top_genes_svm[i].accuracy}")

Genes selected across 12+ model(s). Accuracy: 0.726465692620677
Genes selected across 6+ model(s). Accuracy: 0.8853338265211762
Genes selected across 1+ model(s). Accuracy: 0.9772517107453301

for i in [12, 6, 1]:
    ds_hcc_top_genes_svm[i] = train_test_svm(X_top_genes_ds_hcc[i], y_ds_hcc, n_jobs = -1, verbose = False)
    print(f"Genes selected across {i}+ model(s). Accuracy: {ds_hcc_top_genes_svm[i].accuracy}")

Genes selected across 12+ model(s). Accuracy: 0.6061018795968401
Genes selected across 6+ model(s). Accuracy: 0.8136747480250613
Genes selected across 1+ model(s). Accuracy: 0.9506946336148189

ss_mcf7_top_genes_random_forest: dict[int, TrainedModelWrapper] = {}

ss_hcc_top_genes_random_forest: dict[int, TrainedModelWrapper] = {}

ds_mcf7_top_genes_random_forest: dict[int, TrainedModelWrapper] = {}

ds_hcc_top_genes_random_forest: dict[int, TrainedModelWrapper] = {}

for i in [12, 6, 1]:
    ss_mcf7_top_genes_random_forest[i] = train_test_random_forest(X_top_genes_ss_mcf7[i], y_ss_mcf7, n_jobs = -1, verbose = False)
    print(f"Genes selected across {i}+ model(s). Accuracy: {ss_mcf7_top_genes_random_forest[i].accuracy}")

Genes selected across 12+ model(s). Accuracy: 0.9365079365079365
Genes selected across 6+ model(s). Accuracy: 1.0
Genes selected across 1+ model(s). Accuracy: 1.0

for i in [12, 6, 1]:
    ss_hcc_top_genes_random_forest[i] = train_test_random_forest(X_top_genes_ss_hcc[i], y_ss_hcc, n_jobs = -1, verbose = False)
    print(f"Genes selected across {i}+ model(s). Accuracy: {ss_hcc_top_genes_random_forest[i].accuracy}")

Genes selected across 12+ model(s). Accuracy: 0.9782608695652174
Genes selected across 6+ model(s). Accuracy: 0.9782608695652174
Genes selected across 1+ model(s). Accuracy: 0.9782608695652174

for i in [12, 6, 1]:
    ds_mcf7_top_genes_random_forest[i] = train_test_random_forest(X_top_genes_ds_mcf7[i], y_ds_mcf7, n_jobs = -1, verbose = False)
    print(f"Genes selected across {i}+ model(s). Accuracy: {ds_mcf7_top_genes_random_forest[i].accuracy}")

Genes selected across 12+ model(s). Accuracy: 0.726465692620677
Genes selected across 6+ model(s). Accuracy: 0.884224153874607
Genes selected across 1+ model(s). Accuracy: 0.9726280747179582

for i in [12, 6, 1]:
    ds_hcc_top_genes_random_forest[i] = train_test_random_forest(X_top_genes_ds_hcc[i], y_ds_hcc, n_jobs = -1, verbose = False)
    print(f"Genes selected across {i}+ model(s). Accuracy: {ds_hcc_top_genes_random_forest[i].accuracy}")

Genes selected across 12+ model(s). Accuracy: 0.48597112503405065
Genes selected across 6+ model(s). Accuracy: 0.8193952601470988
Genes selected across 1+ model(s). Accuracy: 0.9406156360664669

ss_mcf7_top_genes_mlp: dict[int, TrainedModelWrapper] = {}

ss_hcc_top_genes_mlp: dict[int, TrainedModelWrapper] = {}

ds_mcf7_top_genes_mlp: dict[int, TrainedModelWrapper] = {}

ds_hcc_top_genes_mlp: dict[int, TrainedModelWrapper] = {}

for i in [12, 6, 1]:
    ss_mcf7_top_genes_mlp[i] = train_test_mlp(X_top_genes_ss_mcf7[i], y_ss_mcf7, n_jobs = -1, verbose = False)
    print(f"Genes selected across {i}+ model(s). Accuracy: {ss_mcf7_top_genes_mlp[i].accuracy}")

Genes selected across 12+ model(s). Accuracy: 0.5079365079365079
Genes selected across 6+ model(s). Accuracy: 0.9206349206349206
Genes selected across 1+ model(s). Accuracy: 0.9682539682539683

for i in [12, 6, 1]:
    ss_hcc_top_genes_mlp[i] = train_test_mlp(X_top_genes_ss_hcc[i], y_ss_hcc, n_jobs = -1, verbose = False)
    print(f"Genes selected across {i}+ model(s). Accuracy: {ss_hcc_top_genes_mlp[i].accuracy}")

Genes selected across 12+ model(s). Accuracy: 0.5434782608695652
Genes selected across 6+ model(s). Accuracy: 0.9130434782608695
Genes selected across 1+ model(s). Accuracy: 0.9347826086956522

for i in [12, 6, 1]:
    ds_mcf7_top_genes_mlp[i] = train_test_mlp(X_top_genes_ds_mcf7[i], y_ds_mcf7, n_jobs = -1, verbose = False)
    print(f"Genes selected across {i}+ model(s). Accuracy: {ds_mcf7_top_genes_mlp[i].accuracy}")

Genes selected across 12+ model(s). Accuracy: 0.726465692620677
Genes selected across 6+ model(s). Accuracy: 0.8875531718143148
Genes selected across 1+ model(s). Accuracy: 0.980395783243943

for i in [12, 6, 1]:
    ds_hcc_top_genes_mlp[i] = train_test_mlp(X_top_genes_ds_hcc[i], y_ds_hcc, n_jobs = -1, verbose = False)
    print(f"Genes selected across {i}+ model(s). Accuracy: {ds_hcc_top_genes_mlp[i].accuracy}")

Genes selected across 12+ model(s). Accuracy: 0.6061018795968401
Genes selected across 6+ model(s). Accuracy: 0.8163988014165078
Genes selected across 1+ model(s). Accuracy: 0.9504222282756742

X_top_pcs_ss_mcf7 = {i: X_pca_ss_mcf7[:, ss_mcf7_top_pcs[i]] for i in range(1, 4)}
X_top_pcs_ss_hcc = {i: X_pca_ss_hcc[:, ss_hcc_top_pcs[i]] for i in range(1, 4)}
X_top_pcs_ds_mcf7 = {i: X_pca_ds_mcf7[:, ds_mcf7_top_pcs[i]] for i in range(1, 4)}
X_top_pcs_ds_hcc = {i: X_pca_ds_hcc[:, ds_hcc_top_pcs[i]] for i in range(1, 4)}

ss_mcf7_top_pcs_logit: dict[int, TrainedModelWrapper] = {}

ss_hcc_top_pcs_logit: dict[int, TrainedModelWrapper] = {}

ds_mcf7_top_pcs_logit: dict[int, TrainedModelWrapper] = {}

ds_hcc_top_pcs_logit: dict[int, TrainedModelWrapper] = {}

for i in range(3, 0, -1):
    ss_mcf7_top_pcs_logit[i] = train_test_logistic_regression(X_top_pcs_ss_mcf7[i], y_ss_mcf7, n_jobs = -1, verbose = False)
    print(f"PCs selected across {i}+ model(s). Accuracy: {ss_mcf7_top_pcs_logit[i].accuracy}")

PCs selected across 3+ model(s). Accuracy: 0.5079365079365079
PCs selected across 2+ model(s). Accuracy: 0.5238095238095238
PCs selected across 1+ model(s). Accuracy: 0.7619047619047619

for i in range(3, 0, -1):
    ss_hcc_top_pcs_logit[i] = train_test_logistic_regression(X_top_pcs_ss_hcc[i], y_ss_hcc, n_jobs = -1, verbose = False)
    print(f"PCs selected across {i}+ model(s). Accuracy: {ss_hcc_top_pcs_logit[i].accuracy}")

PCs selected across 3+ model(s). Accuracy: 0.9130434782608695

PCs selected across 2+ model(s). Accuracy: 0.9347826086956522
PCs selected across 1+ model(s). Accuracy: 0.8913043478260869

for i in range(3, 0, -1):
    ds_mcf7_top_pcs_logit[i] = train_test_logistic_regression(X_top_pcs_ds_mcf7[i], y_ds_mcf7, n_jobs = -1, verbose = False)
    print(f"PCs selected across {i}+ model(s). Accuracy: {ds_mcf7_top_pcs_logit[i].accuracy}")

PCs selected across 3+ model(s). Accuracy: 0.9258368781209543
PCs selected across 2+ model(s). Accuracy: 0.9315701867948956

PCs selected across 1+ model(s). Accuracy: 0.9408174588496394

for i in range(3, 0, -1):
    ds_hcc_top_pcs_logit[i] = train_test_logistic_regression(X_top_pcs_ds_hcc[i], y_ds_hcc, n_jobs = -1, verbose = False)
    print(f"PCs selected across {i}+ model(s). Accuracy: {ds_hcc_top_pcs_logit[i].accuracy}")

PCs selected across 3+ model(s). Accuracy: 0.8673385998365568
PCs selected across 2+ model(s). Accuracy: 0.8921274856987197
PCs selected across 1+ model(s). Accuracy: 0.9199128302914737

ss_mcf7_top_pcs_svm: dict[int, TrainedModelWrapper] = {}

ss_hcc_top_pcs_svm: dict[int, TrainedModelWrapper] = {}

ds_mcf7_top_pcs_svm: dict[int, TrainedModelWrapper] = {}

ds_hcc_top_pcs_svm: dict[int, TrainedModelWrapper] = {}

for i in range(3, 0, -1):
    ss_mcf7_top_pcs_svm[i] = train_test_svm(X_top_pcs_ss_mcf7[i], y_ss_mcf7, n_jobs = -1, verbose = False)
    print(f"PCs selected across {i}+ model(s). Accuracy: {ss_mcf7_top_pcs_svm[i].accuracy}")

PCs selected across 3+ model(s). Accuracy: 0.49206349206349204
PCs selected across 2+ model(s). Accuracy: 0.47619047619047616
PCs selected across 1+ model(s). Accuracy: 0.746031746031746

for i in range(3, 0, -1):
    ss_hcc_top_pcs_svm[i] = train_test_svm(X_top_pcs_ss_hcc[i], y_ss_hcc, n_jobs = -1, verbose = False)
    print(f"PCs selected across {i}+ model(s). Accuracy: {ss_hcc_top_pcs_svm[i].accuracy}")

PCs selected across 3+ model(s). Accuracy: 0.9347826086956522
PCs selected across 2+ model(s). Accuracy: 0.9347826086956522
PCs selected across 1+ model(s). Accuracy: 0.8695652173913043

for i in range(3, 0, -1):
    ds_mcf7_top_pcs_svm[i] = train_test_svm(X_top_pcs_ds_mcf7[i], y_ds_mcf7, n_jobs = -1, verbose = False)
    print(f"PCs selected across {i}+ model(s). Accuracy: {ds_mcf7_top_pcs_svm[i].accuracy}")

PCs selected across 3+ model(s). Accuracy: 0.9238024782689107
PCs selected across 2+ model(s). Accuracy: 0.926391714444239
PCs selected across 1+ model(s). Accuracy: 0.9371185500277418

for i in range(3, 0, -1):
    ds_hcc_top_pcs_svm[i] = train_test_svm(X_top_pcs_ds_hcc[i], y_ds_hcc, n_jobs = -1, verbose = False)
    print(f"PCs selected across {i}+ model(s). Accuracy: {ds_hcc_top_pcs_svm[i].accuracy}")

PCs selected across 3+ model(s). Accuracy: 0.8662489784799782
PCs selected across 2+ model(s). Accuracy: 0.8902206483247072
PCs selected across 1+ model(s). Accuracy: 0.9212748569871969

ss_mcf7_top_pcs_random_forest: dict[int, TrainedModelWrapper] = {}

ss_hcc_top_pcs_random_forest: dict[int, TrainedModelWrapper] = {}

ds_mcf7_top_pcs_random_forest: dict[int, TrainedModelWrapper] = {}

ds_hcc_top_pcs_random_forest: dict[int, TrainedModelWrapper] = {}

for i in range(3, 0, -1):
    ss_mcf7_top_pcs_random_forest[i] = train_test_random_forest(X_top_pcs_ss_mcf7[i], y_ss_mcf7, n_jobs = -1, verbose = False)
    print(f"PCs selected across {i}+ model(s). Accuracy: {ss_mcf7_top_pcs_random_forest[i].accuracy}")

PCs selected across 3+ model(s). Accuracy: 0.9206349206349206
PCs selected across 2+ model(s). Accuracy: 0.9365079365079365
PCs selected across 1+ model(s). Accuracy: 0.9841269841269841

for i in range(3, 0, -1):
    ss_hcc_top_pcs_random_forest[i] = train_test_random_forest(X_top_pcs_ss_hcc[i], y_ss_hcc, n_jobs = -1, verbose = False)
    print(f"PCs selected across {i}+ model(s). Accuracy: {ss_hcc_top_pcs_random_forest[i].accuracy}")

PCs selected across 3+ model(s). Accuracy: 0.9347826086956522
PCs selected across 2+ model(s). Accuracy: 0.9347826086956522
PCs selected across 1+ model(s). Accuracy: 0.9347826086956522

for i in range(3, 0, -1):
    ds_mcf7_top_pcs_random_forest[i] = train_test_random_forest(X_top_pcs_ds_mcf7[i], y_ds_mcf7, n_jobs = -1, verbose = False)
    print(f"PCs selected across {i}+ model(s). Accuracy: {ds_mcf7_top_pcs_random_forest[i].accuracy}")

PCs selected across 3+ model(s). Accuracy: 0.9326798594414648
PCs selected across 2+ model(s). Accuracy: 0.9343443684113186
PCs selected across 1+ model(s). Accuracy: 0.9361938228222675

for i in range(3, 0, -1):
    ds_hcc_top_pcs_random_forest[i] = train_test_random_forest(X_top_pcs_ds_hcc[i], y_ds_hcc, n_jobs = -1, verbose = False)
    print(f"PCs selected across {i}+ model(s). Accuracy: {ds_hcc_top_pcs_random_forest[i].accuracy}")

PCs selected across 3+ model(s). Accuracy: 0.8703350585671479
PCs selected across 2+ model(s). Accuracy: 0.8714246799237265
PCs selected across 1+ model(s). Accuracy: 0.8855897575592482

ss_mcf7_top_pcs_mlp: dict[int, TrainedModelWrapper] = {}

ss_hcc_top_pcs_mlp: dict[int, TrainedModelWrapper] = {}

ds_mcf7_top_pcs_mlp: dict[int, TrainedModelWrapper] = {}

ds_hcc_top_pcs_mlp: dict[int, TrainedModelWrapper] = {}

for i in range(3, 0, -1):
    ss_mcf7_top_pcs_mlp[i] = train_test_mlp(X_top_pcs_ss_mcf7[i], y_ss_mcf7, n_jobs = -1, verbose = False)
    print(f"PCs selected across {i}+ model(s). Accuracy: {ss_mcf7_top_pcs_mlp[i].accuracy}")

PCs selected across 3+ model(s). Accuracy: 0.7619047619047619
PCs selected across 2+ model(s). Accuracy: 0.8571428571428571
PCs selected across 1+ model(s). Accuracy: 0.8095238095238095

for i in range(3, 0, -1):
    ss_hcc_top_pcs_mlp[i] = train_test_mlp(X_top_pcs_ss_hcc[i], y_ss_hcc, n_jobs = -1, verbose = False)
    print(f"PCs selected across {i}+ model(s). Accuracy: {ss_hcc_top_pcs_mlp[i].accuracy}")

PCs selected across 3+ model(s). Accuracy: 0.9347826086956522
PCs selected across 2+ model(s). Accuracy: 0.9130434782608695
PCs selected across 1+ model(s). Accuracy: 0.9565217391304348

for i in range(3, 0, -1):
    ds_mcf7_top_pcs_mlp[i] = train_test_mlp(X_top_pcs_ds_mcf7[i], y_ds_mcf7, n_jobs = -1, verbose = False)
    print(f"PCs selected across {i}+ model(s). Accuracy: {ds_mcf7_top_pcs_mlp[i].accuracy}")

PCs selected across 3+ model(s). Accuracy: 0.9472905492879601
PCs selected across 2+ model(s). Accuracy: 0.953393748844091
PCs selected across 1+ model(s). Accuracy: 0.9628259663399297

for i in range(3, 0, -1):
    ds_hcc_top_pcs_mlp[i] = train_test_mlp(X_top_pcs_ds_hcc[i], y_ds_hcc, n_jobs = -1, verbose = False)
    print(f"PCs selected across {i}+ model(s). Accuracy: {ds_hcc_top_pcs_mlp[i].accuracy}")

PCs selected across 3+ model(s). Accuracy: 0.8943067284118769
PCs selected across 2+ model(s). Accuracy: 0.9114682647779897
PCs selected across 1+ model(s). Accuracy: 0.9229092890220648

def compare_accuracies(logits: dict[int, TrainedModelWrapper], svms: dict[int, TrainedModelWrapper], random_forests: dict[int, TrainedModelWrapper], mlps: dict[int, TrainedModelWrapper], feature_type: str = "feature"):
    max_key = max(logits, key = lambda k: logits[k].accuracy)
    max_accuracy = logits[max_key].accuracy

    print(f"Logistic regression accuracy: {max_accuracy} with {feature_type}s selected across {max_key}+ models.")
    
    max_key = max(svms, key = lambda k: svms[k].accuracy)
    max_accuracy = svms[max_key].accuracy

    print(f"SVM accuracy: {max_accuracy} with {feature_type}s selected across {max_key}+ models.")
    
    max_key = max(random_forests, key = lambda k: random_forests[k].accuracy)
    max_accuracy = random_forests[max_key].accuracy

    print(f"Random forest accuracy: {max_accuracy} with {feature_type}s selected across {max_key}+ models.")
    
    max_key = max(mlps, key = lambda k: mlps[k].accuracy)
    max_accuracy = mlps[max_key].accuracy

    print(f"Multilayer perceptron accuracy: {max_accuracy} with {feature_type}s selected across {max_key}+ models.")

compare_accuracies(ss_mcf7_top_genes_logit, ss_mcf7_top_genes_svm, ss_mcf7_top_genes_random_forest, ss_mcf7_top_genes_mlp, "gene")

Logistic regression accuracy: 1.0 with genes selected across 6+ models.
SVM accuracy: 1.0 with genes selected across 6+ models.
Random forest accuracy: 1.0 with genes selected across 6+ models.
Multilayer perceptron accuracy: 0.9841269841269841 with genes selected across 1+ models.

compare_accuracies(ss_hcc_top_genes_logit, ss_hcc_top_genes_svm, ss_hcc_top_genes_random_forest, ss_hcc_top_genes_mlp, "gene")

Logistic regression accuracy: 0.9782608695652174 with genes selected across 12+ models.
SVM accuracy: 0.9782608695652174 with genes selected across 12+ models.
Random forest accuracy: 0.9782608695652174 with genes selected across 6+ models.
Multilayer perceptron accuracy: 0.8695652173913043 with genes selected across 6+ models.

compare_accuracies(ds_mcf7_top_genes_logit, ds_mcf7_top_genes_svm, ds_mcf7_top_genes_random_forest, ds_mcf7_top_genes_mlp, "gene")

Logistic regression accuracy: 0.9757721472165711 with genes selected across 1+ models.
SVM accuracy: 0.9750323654521916 with genes selected across 1+ models.
Random forest accuracy: 0.9726280747179582 with genes selected across 1+ models.
Multilayer perceptron accuracy: 0.9783613833918994 with genes selected across 1+ models.

compare_accuracies(ds_hcc_top_genes_logit, ds_hcc_top_genes_svm, ds_hcc_top_genes_random_forest, ds_mcf7_top_genes_mlp, "gene")

Logistic regression accuracy: 0.9512394442931081 with genes selected across 1+ models.
SVM accuracy: 0.9515118496322528 with genes selected across 1+ models.
Random forest accuracy: 0.9441569054753474 with genes selected across 1+ models.
Multilayer perceptron accuracy: 0.9783613833918994 with genes selected across 1+ models.

print("Logistic regression accuracy:", ss_mcf7_pca_logit.accuracy)
print("SVM accuracy:", ss_mcf7_pca_svm.accuracy)
print("Random forest accuracy:", ss_mcf7_pca_random_forest.accuracy)
print("Multilayer perceptron accuracy:", ss_mcf7_pca_mlp.accuracy)

Logistic regression accuracy: 1.0
SVM accuracy: 1.0
Random forest accuracy: 1.0
Multilayer perceptron accuracy: 0.9682539682539683

print("Logistic regression accuracy:", ss_hcc_pca_logit.accuracy)
print("SVM accuracy:", ss_hcc_pca_svm.accuracy)
print("Random forest accuracy:", ss_hcc_pca_random_forest.accuracy)
print("Multilayer perceptron accuracy:", ss_hcc_pca_mlp.accuracy)

Logistic regression accuracy: 0.9782608695652174
SVM accuracy: 0.9782608695652174
Random forest accuracy: 0.9782608695652174
Multilayer perceptron accuracy: 0.8913043478260869

print("Logistic regression accuracy:", ds_mcf7_pca_logit.accuracy)
print("SVM accuracy:", ds_mcf7_pca_svm.accuracy)
print("Random forest accuracy:", ds_mcf7_pca_random_forest.accuracy)
print("Multilayer perceptron accuracy:", ds_mcf7_pca_mlp.accuracy)

Logistic regression accuracy: 0.975957092657666
SVM accuracy: 0.9755872017754762
Random forest accuracy: 0.926206769003144
Multilayer perceptron accuracy: 0.9776216016275199

print("Logistic regression accuracy:", ds_hcc_pca_logit.accuracy)
print("SVM accuracy:", ds_hcc_pca_svm.accuracy)
print("Random forest accuracy:", ds_hcc_pca_random_forest.accuracy)
print("Multilayer perceptron accuracy:", ds_hcc_pca_mlp.accuracy)

Logistic regression accuracy: 0.9566875510760011
SVM accuracy: 0.954508308362844
Random forest accuracy: 0.8948515390901661
Multilayer perceptron accuracy: 0.9591391991283029

print("Logistic regression accuracy:", np.mean([ss_mcf7_pca_logit.accuracy, ss_hcc_pca_logit.accuracy, ds_mcf7_pca_logit.accuracy, ds_hcc_pca_logit.accuracy]))
print("SVM accuracy:", np.mean([ss_mcf7_pca_svm.accuracy, ss_hcc_pca_svm.accuracy, ds_mcf7_pca_svm.accuracy, ds_hcc_pca_svm.accuracy]))
print("Random forest accuracy:", np.mean([ss_mcf7_pca_random_forest.accuracy, ss_hcc_pca_random_forest.accuracy, ds_mcf7_pca_random_forest.accuracy, ds_hcc_pca_random_forest.accuracy]))
print("Multilayer perceptron accuracy:", np.mean([ss_mcf7_pca_mlp.accuracy, ss_hcc_pca_mlp.accuracy, ds_mcf7_pca_mlp.accuracy, ds_hcc_pca_mlp.accuracy]))

Logistic regression accuracy: 0.9777263783247212
SVM accuracy: 0.9770890949258844
Random forest accuracy: 0.9498297944146319
Multilayer perceptron accuracy: 0.9490797792089696

compare_accuracies(ss_mcf7_top_pcs_logit, ss_mcf7_top_pcs_svm, ss_mcf7_top_pcs_random_forest, ss_mcf7_top_pcs_mlp, "PC")

Logistic regression accuracy: 0.7936507936507936 with PCs selected across 2+ models.
SVM accuracy: 0.7619047619047619 with PCs selected across 2+ models.
Random forest accuracy: 0.9523809523809523 with PCs selected across 2+ models.
Multilayer perceptron accuracy: 0.8888888888888888 with PCs selected across 2+ models.

compare_accuracies(ss_hcc_top_pcs_logit, ss_hcc_top_pcs_svm, ss_hcc_top_pcs_random_forest, ss_hcc_top_pcs_mlp, "PC")

Logistic regression accuracy: 0.9347826086956522 with PCs selected across 3+ models.
SVM accuracy: 0.9130434782608695 with PCs selected across 3+ models.
Random forest accuracy: 0.9130434782608695 with PCs selected across 2+ models.
Multilayer perceptron accuracy: 0.9130434782608695 with PCs selected across 3+ models.

compare_accuracies(ds_mcf7_top_pcs_logit, ds_mcf7_top_pcs_svm, ds_mcf7_top_pcs_random_forest, ds_mcf7_top_pcs_mlp, "PC")

Logistic regression accuracy: 0.9347142592935084 with PCs selected across 1+ models.
SVM accuracy: 0.9295357869428519 with PCs selected across 1+ models.
Random forest accuracy: 0.9374884409099316 with PCs selected across 2+ models.
Multilayer perceptron accuracy: 0.9515442944331423 with PCs selected across 1+ models.

compare_accuracies(ds_hcc_top_pcs_logit, ds_hcc_top_pcs_svm, ds_hcc_top_pcs_random_forest, ds_hcc_top_pcs_mlp, "PC")

Logistic regression accuracy: 0.925905747752656 with PCs selected across 1+ models.
SVM accuracy: 0.925088531735222 with PCs selected across 1+ models.
Random forest accuracy: 0.892672296377009 with PCs selected across 1+ models.
Multilayer perceptron accuracy: 0.9299918278398257 with PCs selected across 1+ models.

class SimpleMajorityVoteClassifier():   
    def __init__(self, models: list[TrainedModelWrapper]):
        """
        models: list of pretrained classifiers with .predict() method
        """
        self.models = models
        
        test_sets = [(model.X_test, model.y_test) for model in self.models]
        self.assert_test_sets_equal(test_sets)
        
        self.X_test = models[0].X_test
        self.y_test = models[0].y_test
        
        self.features = self.X_test.columns
        self.accuracy = None
        
    def predict(self, X):
        if missing := set(self.features) - set(X.columns):
            raise ValueError(f"Missing columns:", missing)
        
        predictions = [model.predict(X) for model in self.models]
        predictions = np.vstack(predictions)

        predictions_T = predictions.T

        majority_votes = []

        for sample_preds in predictions_T:
            values, counts = np.unique(sample_preds, return_counts = True)
            best_label = values[np.argmax(counts)]
            majority_votes.append(best_label)

        return np.array(majority_votes)
    
    def assert_test_sets_equal(self, test_sets: list):
        ref_X, ref_y = test_sets[0]
        for i, (X, y) in enumerate(test_sets[1:], start=1):
            if not (np.array_equal(ref_X, X) and np.array_equal(ref_y, y)):
                raise ValueError(f"Test set {i} does not match the reference test set.")
    
    def test(self, X_test: Any | None = None, y_test: Any | None = None, verbose: bool = True):
        if not X_test or not y_test:
            X_test = self.X_test
            y_test = self.y_test
        
        self.accuracy = test_model(self, X_test, y_test, verbose)
        return self.accuracy

ss_mcf7_simple_ensemble = SimpleMajorityVoteClassifier([
    ss_mcf7_top_genes_logit[1],
    ss_mcf7_top_genes_svm[1],
    ss_mcf7_top_genes_random_forest[1]
])
ss_mcf7_simple_ensemble.test()

========================= Testing =========================
Confusion matrix:
             Predicted Hypo  Predicted Norm
Actual Hypo              31               0
Actual Norm               0              32
Accuracy: 1.0
Classification report:
               precision    recall  f1-score   support

        Hypo       1.00      1.00      1.00        31
        Norm       1.00      1.00      1.00        32

    accuracy                           1.00        63
   macro avg       1.00      1.00      1.00        63
weighted avg       1.00      1.00      1.00        63

1.0

ss_hcc_simple_ensemble = SimpleMajorityVoteClassifier([
    ss_hcc_top_genes_logit[1],
    ss_hcc_top_genes_svm[1],
    ss_hcc_top_genes_random_forest[1]
])
ss_hcc_simple_ensemble.test()

========================= Testing =========================
Confusion matrix:
             Predicted Hypo  Predicted Norm
Actual Hypo              24               1
Actual Norm               0              21
Accuracy: 0.9782608695652174
Classification report:
               precision    recall  f1-score   support

        Hypo       1.00      0.96      0.98        25
        Norm       0.95      1.00      0.98        21

    accuracy                           0.98        46
   macro avg       0.98      0.98      0.98        46
weighted avg       0.98      0.98      0.98        46

0.9782608695652174

ds_mcf7_simple_ensemble = SimpleMajorityVoteClassifier([
    ds_mcf7_top_genes_logit[1],
    ds_mcf7_top_genes_svm[1],
    ds_mcf7_top_genes_random_forest[1],
    ds_mcf7_top_genes_mlp[1]
])
ds_mcf7_simple_ensemble.test()

========================= Testing =========================
Confusion matrix:
             Predicted Hypo  Predicted Norm
Actual Hypo            2164              66
Actual Norm              49            3128
Accuracy: 0.9787312742740891
Classification report:
               precision    recall  f1-score   support

        Hypo       0.98      0.97      0.97      2230
        Norm       0.98      0.98      0.98      3177

    accuracy                           0.98      5407
   macro avg       0.98      0.98      0.98      5407
weighted avg       0.98      0.98      0.98      5407

0.9787312742740891

ds_hcc_simple_ensemble = SimpleMajorityVoteClassifier([
    ds_hcc_top_genes_logit[1],
    ds_hcc_top_genes_svm[1],
    ds_hcc_top_genes_random_forest[1],
    ds_hcc_top_genes_mlp[1]
])
ds_hcc_simple_ensemble.test()

========================= Testing =========================
Confusion matrix:
             Predicted Hypo  Predicted Norm
Actual Hypo            2126              99
Actual Norm              77            1369
Accuracy: 0.952056660310542
Classification report:
               precision    recall  f1-score   support

        Hypo       0.97      0.96      0.96      2225
        Norm       0.93      0.95      0.94      1446

    accuracy                           0.95      3671
   macro avg       0.95      0.95      0.95      3671
weighted avg       0.95      0.95      0.95      3671

0.952056660310542

class WeightedMajorityVoteClassifier():   
    def __init__(self, models: list[TrainedModelWrapper]):
        """
        models: list of pretrained classifiers with .predict() method
        """
        self.models = models
        
        test_sets = [(model.X_test, model.y_test) for model in self.models]
        self.assert_test_sets_equal(test_sets)
        
        self.X_test = models[0].X_test
        self.y_test = models[0].y_test
        
        self.features = self.X_test.columns
        self.accuracy = None
        
    def predict(self, X):
        if missing := set(self.features) - set(X.columns):
            raise ValueError(f"Missing columns:", missing)
        
        predictions = [model.predict(X) for model in self.models]
        predictions = np.vstack(predictions)
        
        weights = np.array([model.accuracy for model in self.models])

        predictions_T = predictions.T

        weighted_votes = []

        for sample_preds in predictions_T:
            unique_labels = np.unique(sample_preds)
            label_weights = {label: 0.0 for label in unique_labels}

            for pred, weight in zip(sample_preds, weights):
                label_weights[pred] += weight

            best_label = max(label_weights, key = label_weights.get)
            weighted_votes.append(best_label)

        return np.array(weighted_votes)
    
    def assert_test_sets_equal(self, test_sets: list):
        ref_X, ref_y = test_sets[0]
        for i, (X, y) in enumerate(test_sets[1:], start=1):
            if not (np.array_equal(ref_X, X) and np.array_equal(ref_y, y)):
                raise ValueError(f"Test set {i} does not match the reference test set.")
    
    def test(self, X_test: Any | None = None, y_test: Any | None = None, verbose: bool = True):
        if not X_test or not y_test:
            X_test = self.X_test
            y_test = self.y_test
        
        self.accuracy = test_model(self, X_test, y_test, verbose)
        return self.accuracy

class GeneralizedMajorityVoteClassifier():
    def __init__(self, models: list[SimpleMajorityVoteClassifier]):
        """
        models: list of pretrained classifiers with .predict() method
        """
        self.models = models
        
        self.test_sets = [(model.X_test, model.y_test) for model in self.models]
        self.features = [X.columns for X, _ in self.test_sets]
    
    def predict(self, X):
        input_features = set(X.columns)
        
        predictions = [model.predict(X.loc[:, model.features]) for model in self.models if not set(model.features) - input_features]
        predictions = np.vstack(predictions)

        predictions_T = predictions.T

        majority_votes = []

        for sample_preds in predictions_T:
            values, counts = np.unique(sample_preds, return_counts = True)
            best_label = values[np.argmax(counts)]
            majority_votes.append(best_label)

        return np.array(majority_votes)

    def test(self, X_test, y_test, verbose: bool = True):
        return test_model(self, X_test, y_test, verbose)

generalized_classifier = GeneralizedMajorityVoteClassifier([ss_mcf7_simple_ensemble, ss_hcc_simple_ensemble, ds_mcf7_simple_ensemble, ds_hcc_simple_ensemble])
generalized_classifier_scores = {}

generalized_classifier_scores["ss_mcf7"] = generalized_classifier.test(X_ss_mcf7, y_ss_mcf7)

========================= Testing =========================
Confusion matrix:
             Predicted Hypo  Predicted Norm
Actual Hypo             124               0
Actual Norm               0             126
Accuracy: 1.0
Classification report:
               precision    recall  f1-score   support

        Hypo       1.00      1.00      1.00       124
        Norm       1.00      1.00      1.00       126

    accuracy                           1.00       250
   macro avg       1.00      1.00      1.00       250
weighted avg       1.00      1.00      1.00       250

generalized_classifier_scores["ss_hcc"] = generalized_classifier.test(X_ss_hcc, y_ss_hcc)

========================= Testing =========================
Confusion matrix:
             Predicted Hypo  Predicted Norm
Actual Hypo              96               1
Actual Norm               0              85
Accuracy: 0.9945054945054945
Classification report:
               precision    recall  f1-score   support

        Hypo       1.00      0.99      0.99        97
        Norm       0.99      1.00      0.99        85

    accuracy                           0.99       182
   macro avg       0.99      0.99      0.99       182
weighted avg       0.99      0.99      0.99       182

generalized_classifier_scores["ds_mcf7"] = generalized_classifier.test(X_ds_mcf7, y_ds_mcf7)

========================= Testing =========================
Confusion matrix:
             Predicted Hypo  Predicted Norm
Actual Hypo            8806             115
Actual Norm             107           12598
Accuracy: 0.9897345787478036
Classification report:
               precision    recall  f1-score   support

        Hypo       0.99      0.99      0.99      8921
        Norm       0.99      0.99      0.99     12705

    accuracy                           0.99     21626
   macro avg       0.99      0.99      0.99     21626
weighted avg       0.99      0.99      0.99     21626

generalized_classifier_scores["ds_hcc"] = generalized_classifier.test(X_ds_hcc, y_ds_hcc)

========================= Testing =========================
Confusion matrix:
             Predicted Hypo  Predicted Norm
Actual Hypo            8721             178
Actual Norm             189            5594
Accuracy: 0.9750034055305816
Classification report:
               precision    recall  f1-score   support

        Hypo       0.98      0.98      0.98      8899
        Norm       0.97      0.97      0.97      5783

    accuracy                           0.98     14682
   macro avg       0.97      0.97      0.97     14682
weighted avg       0.97      0.98      0.97     14682

def plot_accuracies(
    data_dict: dict[str, dict[str, float]],
    title: str = "Model Accuracies",
    ylabel: str = "Accuracy (%)",
    xlabel = "Models"
):
    colors = plt.cm.tab10.colors
    category_color_map = {}
    entries = []

    # Flatten and store (model_name, accuracy, category)
    for i, (category, models) in enumerate(data_dict.items()):
        category_color_map[category] = colors[i % len(colors)]
        for model_name, acc in models.items():
            acc = acc * 100 if 0 <= acc <= 1 else acc
            entries.append((model_name, acc, category))

    # Sort by accuracy (descending)
    entries.sort(key = lambda x: x[1], reverse = True)

    # Extract values for plotting
    model_labels = [f"{model}" for model, _, _ in entries]
    accuracies = [accuracy for _, accuracy, _ in entries]
    categories = [category for _, _, category in entries]
    bar_colors = [category_color_map[category] for category in categories]
    x_positions = np.arange(len(entries))

    plt.figure(figsize = (max(10, len(entries) * 0.6), 8))

    bars = plt.bar(x_positions, accuracies, color = bar_colors, edgecolor = "black")

    # Set Y-limits before placing text
    min_y = min(accuracies)
    max_y = max(accuracies)
    if max_y - min_y < 5:
        padding = max(1.0, (max_y - min_y) * 0.2)
    else:
        padding = (max_y - min_y) * 0.2
    plt.ylim(min_y - padding, max_y + padding)

    # Add accuracy labels above bars
    for x, acc in zip(x_positions, accuracies):
        label_y = acc + (padding * 0.2)
        plt.text(x, label_y, f"{acc:.1f}%", ha = "center", va = "top", fontsize = 9)

    # X-axis
    plt.xticks(x_positions, model_labels, rotation = 45, ha = "right")
    plt.ylabel(ylabel)
    plt.xlabel(xlabel)
    plt.title(title)
    plt.grid(axis = "y", linestyle = "--", alpha = 0.7)

    # Legend
    handles = [plt.Rectangle((0, 0), 1, 1, color = category_color_map[category]) for category in category_color_map]
    labels = list(category_color_map.keys())
    plt.legend(handles, labels, title = "Category")

    plt.tight_layout()
    plt.show()

plot_accuracies(
    {
        "PCA": {
            "Logistic Regression": ss_mcf7_pca_logit.accuracy,
            "SVM": ss_mcf7_pca_svm.accuracy,
            "Random Forest": ss_mcf7_pca_random_forest.accuracy,
            "Multilayer Perceptron": ss_mcf7_pca_mlp.accuracy
        },
        "Top PCs": {
            "Logistic Regression": ss_mcf7_top_pcs_logit[1].accuracy,
            "SVM": ss_mcf7_top_pcs_svm[1].accuracy,
            "Random Forest": ss_mcf7_top_pcs_random_forest[1].accuracy,
            "Multilayer Perceptron": ss_mcf7_top_pcs_mlp[1].accuracy
        },
        "Top Genes": {
            "Logistic Regression": ss_mcf7_top_genes_logit[1].accuracy,
            "SVM": ss_mcf7_top_genes_svm[1].accuracy,
            "Random Forest": ss_mcf7_top_genes_random_forest[1].accuracy,
            "Multilayer Perceptron": ss_mcf7_top_genes_mlp[1].accuracy
        },
        "Ensemble": {
            "Simple": ss_mcf7_simple_ensemble.accuracy,
            "Generalized": generalized_classifier_scores["ss_mcf7"]
        }
    },
    "Model Accuracies: SmartSeq MCF7"
)

plot_accuracies(
    {
        "PCA": {
            "Logistic Regression": ss_hcc_pca_logit.accuracy,
            "SVM": ss_hcc_pca_svm.accuracy,
            "Random Forest": ss_hcc_pca_random_forest.accuracy,
            "Multilayer Perceptron": ss_hcc_pca_mlp.accuracy
        },
        "Top PCs": {
            "Logistic Regression": ss_hcc_top_pcs_logit[1].accuracy,
            "SVM": ss_hcc_top_pcs_svm[1].accuracy,
            "Random Forest": ss_hcc_top_pcs_random_forest[1].accuracy,
            "Multilayer Perceptron": ss_hcc_top_pcs_mlp[1].accuracy
        },
        "Top Genes": {
            "Logistic Regression": ss_hcc_top_genes_logit[1].accuracy,
            "SVM": ss_hcc_top_genes_svm[1].accuracy,
            "Random Forest": ss_hcc_top_genes_random_forest[1].accuracy,
            "Multilayer Perceptron": ss_hcc_top_genes_mlp[1].accuracy
        },
        "Ensemble": {
            "Simple": ss_hcc_simple_ensemble.accuracy,
            "Generalized": generalized_classifier_scores["ss_hcc"]
        }
    },
    "Model Accuracies: SmartSeq HCC"
)

plot_accuracies(
    {
        "PCA": {
            "Logistic Regression": ds_mcf7_pca_logit.accuracy,
            "SVM": ds_mcf7_pca_svm.accuracy,
            "Random Forest": ds_mcf7_pca_random_forest.accuracy,
            "Multilayer Perceptron": ds_mcf7_pca_mlp.accuracy
        },
        "Top PCs": {
            "Logistic Regression": ds_mcf7_top_pcs_logit[1].accuracy,
            "SVM": ds_mcf7_top_pcs_svm[1].accuracy,
            "Random Forest": ds_mcf7_top_pcs_random_forest[1].accuracy,
            "Multilayer Perceptron": ds_mcf7_top_pcs_mlp[1].accuracy
        },
        "Top Genes": {
            "Logistic Regression": ds_mcf7_top_genes_logit[1].accuracy,
            "SVM": ds_mcf7_top_genes_svm[1].accuracy,
            "Random Forest": ds_mcf7_top_genes_random_forest[1].accuracy,
            "Multilayer Perceptron": ds_mcf7_top_genes_mlp[1].accuracy
        },
        "Ensemble": {
            "Simple": ds_mcf7_simple_ensemble.accuracy,
            "Generalized": generalized_classifier_scores["ds_mcf7"]
        }
    },
    "Model Accuracies: DropSeq MCF7"
)

plot_accuracies(
    {
        "PCA": {
            "Logistic Regression": ds_hcc_pca_logit.accuracy,
            "SVM": ds_hcc_pca_svm.accuracy,
            "Random Forest": ds_hcc_pca_random_forest.accuracy,
            "Multilayer Perceptron": ds_hcc_pca_mlp.accuracy
        },
        "Top PCs": {
            "Logistic Regression": ds_hcc_top_pcs_logit[1].accuracy,
            "SVM": ds_hcc_top_pcs_svm[1].accuracy,
            "Random Forest": ds_hcc_top_pcs_random_forest[1].accuracy,
            "Multilayer Perceptron": ds_hcc_top_pcs_mlp[1].accuracy
        },
        "Top Genes": {
            "Logistic Regression": ds_hcc_top_genes_logit[1].accuracy,
            "SVM": ds_hcc_top_genes_svm[1].accuracy,
            "Random Forest": ds_hcc_top_genes_random_forest[1].accuracy,
            "Multilayer Perceptron": ds_hcc_top_genes_mlp[1].accuracy
        },
        "Ensemble": {
            "Simple": ds_hcc_simple_ensemble.accuracy,
            "Generalized": generalized_classifier_scores["ds_hcc"]
        }
    },
    "Model Accuracies: DropSeq HCC"
)

dataset_types = ["SmartSeq MCF7", "SmartSeq HCC", "DropSeq MCF7", "DropSeq HCC"]
for i in range(0, 4):
    print(f"{dataset_types[i]} =========================================================")
    for model in generalized_classifier.models[i].models:
        print(model.model)
    print()

SmartSeq MCF7 =========================================================
LogisticRegression(C=0.01, max_iter=20000, n_jobs=-1, random_state=10)
LinearSVC(C=0.025, max_iter=10000, random_state=10)
RandomForestClassifier(max_depth=5, n_estimators=25, n_jobs=-1, random_state=10)

SmartSeq HCC =========================================================
LogisticRegression(C=0.01, max_iter=20000, n_jobs=-1, random_state=10)
LinearSVC(C=0.025, max_iter=10000, random_state=10)
RandomForestClassifier(max_depth=5, n_estimators=25, n_jobs=-1, random_state=10)

DropSeq MCF7 =========================================================
LogisticRegression(C=1, max_iter=20000, n_jobs=-1, random_state=10,
                   solver='sag')
LinearSVC(C=0.025, max_iter=10000, random_state=10)
RandomForestClassifier(class_weight='balanced', max_depth=30, n_estimators=400,
                       n_jobs=-1, random_state=10)
MLPClassifier(alpha=0.001, early_stopping=True, hidden_layer_sizes=(200,),
              max_iter=500, random_state=10)

DropSeq HCC =========================================================
LogisticRegression(C=1, max_iter=20000, n_jobs=-1, random_state=10,
                   solver='sag')
LinearSVC(C=0.025, max_iter=10000, random_state=10)
RandomForestClassifier(class_weight='balanced', max_depth=20,
                       min_samples_leaf=2, min_samples_split=10,
                       n_estimators=300, n_jobs=-1, random_state=10)
MLPClassifier(early_stopping=True, hidden_layer_sizes=(200,), max_iter=500,
              random_state=10)

ss_mcf7_test = pd.read_csv("AILab2025/SmartSeq/MCF7_SmartS_Filtered_Normalised_3000_Data_test_anonim.txt", delimiter = " ", engine = "python", index_col = 0)
ss_hcc_test = pd.read_csv("AILab2025/SmartSeq/HCC1806_SmartS_Filtered_Normalised_3000_Data_test_anonim.txt", delimiter = " ", engine = "python", index_col = 0)
ds_mcf7_test = pd.read_csv("AILab2025/DropSeq/MCF7_Filtered_Normalised_3000_Data_test_anonim.txt", delimiter = " ", engine = "python", index_col = 0)
ds_hcc_test = pd.read_csv("AILab2025/DropSeq/HCC1806_Filtered_Normalised_3000_Data_test_anonim.txt", delimiter = " ", engine = "python", index_col = 0)

ss_mcf7_test_predictions = generalized_classifier.predict(ss_mcf7_test.T)
ss_hcc_test_predictions = generalized_classifier.predict(ss_hcc_test.T)
ds_mcf7_test_predictions = generalized_classifier.predict(ds_mcf7_test.T)
ds_hcc_test_predictions = generalized_classifier.predict(ds_hcc_test.T)

np.savetxt("test_predictions_ss_mcf7.tsv", ss_mcf7_test_predictions, delimiter = "\t", fmt = "%s")
np.savetxt("test_predictions_ss_hcc.tsv", ss_hcc_test_predictions, delimiter = "\t", fmt = "%s")
np.savetxt("test_predictions_ds_mcf7.tsv", ds_mcf7_test_predictions, delimiter = "\t", fmt = "%s")
np.savetxt("test_predictions_ds_hcc.tsv", ds_hcc_test_predictions, delimiter = "\t", fmt = "%s")

# Number of common genes
def common_genes(data1, data2, name1="Dataset 1", name2="Dataset 2"):
    common_genes = data1.index.intersection(data2.index)
    print(f"Number of common genes in {name1} and {name2}: {len(common_genes)}")

common_genes(ss_mcf7_norm, ss_hcc_norm, "ss_mcf7_norm", "ss_hcc_norm")
common_genes(ds_mcf7_norm, ds_hcc_norm, "ds_mcf7_norm", "ds_hcc_norm")
common_genes(ss_mcf7_norm, ds_mcf7_norm, "ss_mcf7_norm", "ds_mcf7_norm")
common_genes(ss_hcc_norm, ds_hcc_norm, "ss_hcc_norm", "ds_hcc_norm")

Number of common genes in ss_mcf7_norm and ss_hcc_norm: 1208
Number of common genes in ds_mcf7_norm and ds_hcc_norm: 834
Number of common genes in ss_mcf7_norm and ds_mcf7_norm: 496
Number of common genes in ss_hcc_norm and ds_hcc_norm: 516

def train_cross_dataset_classifier(
    df_train: pd.DataFrame,
    df_test: pd.DataFrame,
    model_func: Callable,
    test_func: Callable,
    random_state: int = 42,
    use_pca: bool = False,
    pca_var_threshold: float = 0.95,
    verbose=True
) -> tuple[ClassifierMixin, np.ndarray, np.ndarray, list, list]:
    """
    Trains a classifier on one dataset and evaluates it on another after optional PCA alignment.

    Args:
        df_train (pd.DataFrame): Training gene expression data (genes x cells).
        df_test (pd.DataFrame): Testing gene expression data (genes x cells).
        model_func (Callable): Function like train_svm(X_train, y_train, ...) returning a trained model.
        test_func (Callable): Function to test the models.
        random_state (int): Random seed.
        use_pca (bool): Whether to apply PCA before training.
        pca_var_threshold (float): Variance threshold for PCA if used.

    Returns:
        model: Trained model.
        X_train, X_test: Input data (PCA-transformed or raw).
        y_train, y_test: Labels.
    """
    # Align common genes
    common_genes = df_train.index.intersection(df_test.index)
    df_train_aligned = df_train.loc[common_genes]
    df_test_aligned = df_test.loc[common_genes]

    assert list(df_train_aligned.index) == list(df_test_aligned.index), "Gene alignment failed"

    # Transpose to cells × genes
    X_train = df_train_aligned.T
    X_test = df_test_aligned.T

    # Optionally apply PCA
    # Can the axes of variation in X_train explain the structure of X_test?
    if use_pca:
        pca = PCA(n_components=pca_var_threshold)
        X_train = pca.fit_transform(X_train)  # Learn principal components from training data
        X_test = pca.transform(X_test)  # Apply same components to testing data

    # Extract labels
    y_train = ['Hypo' if 'hypo' in name.lower() else 'Norm' for name in df_train_aligned.columns]
    y_test = ['Hypo' if 'hypo' in name.lower() else 'Norm' for name in df_test_aligned.columns]

    # Train the model
    model = model_func(X_train=X_train, y_train=y_train, random_state=random_state, n_jobs=-1, verbose=verbose)

    # Evaluate the model
    if test_func is not None:
        test_func(model=model, X_test=X_test, y_test=y_test, verbose=verbose)

    return model, X_train, X_test, y_train, y_test

def select_best_cross_dataset_model(
    df_train: pd.DataFrame,
    df_test: pd.DataFrame,
    train_funcs: list[Callable],
    test_func: Callable,
    use_pca: bool = False,
    pca_var_threshold: float = 0.95,
    random_state: int = 42,
    verbose=False
):
    """
    Trains multiple models cross-dataset and selects the best one by test accuracy.

    Args:
        df_train, df_test: Gene expression dataframes.
        model_pairs: List of (train_func, test_func).
        use_pca: Whether to apply PCA.
        pca_var_threshold: % variance to retain in PCA.
        random_state: Random seed.

    Returns:
        Prints the best model info and returns (model_name, accuracy).
    """
    best_accuracy = -1
    best_result = None
    best_model_info = {}

    for train_func in train_funcs:
        model_name = train_func.__name__.replace("train_", "").replace("_", " ").title()

        # Wrap train_func to suppress training output
        wrapped_train_func = lambda *args, **kwargs: train_func(*args, **{**kwargs, "verbose": False})

        model, X_train, X_test, y_train, y_test = train_cross_dataset_classifier(
            df_train=df_train,
            df_test=df_test,
            model_func=wrapped_train_func,
            test_func=None,  # skip evaluation for now
            use_pca=use_pca,
            pca_var_threshold=pca_var_threshold,
            random_state=random_state,
            verbose=False
        )

        # Compute accuracy manually
        predictions = model.predict(X_test)
        accuracy = accuracy_score(y_test, predictions)

        print(f"{model_name} test accuracy: {accuracy:.4f}")

        if accuracy > best_accuracy:
            best_accuracy = accuracy
            best_result = (model_name, accuracy)
            best_model_info = {
                "train_func": train_func,
                "test_func": test_func,
                "X_test": X_test,
                "y_test": y_test,
                "model": model
            }

    # Re-run training and testing for best model if verbose=True
    if verbose:
        print(f"\n============ Best Model: {best_result[0]} ============")
        best_model_info["train_func"](
            X_train=X_train,
            y_train=y_train,
            random_state=random_state,
            n_jobs=-1,
            verbose=verbose
        )
        best_model_info["test_func"](
            model=best_model_info["model"],
            X_test=best_model_info["X_test"],
            y_test=best_model_info["y_test"]
        )

    return best_result

# Find the highest accuracy model for MCF7, training on non-reduced Drop-seq data and testing on Smart-seq
select_best_cross_dataset_model(ds_mcf7_norm, ss_mcf7_norm, [train_svm, train_logistic_regression], test_model, use_pca=False)

Svm test accuracy: 0.9680
Logistic Regression test accuracy: 0.9800

('Logistic Regression', 0.98)

# With PCA reduced data
select_best_cross_dataset_model(ds_mcf7_norm, ss_mcf7_norm, [train_svm, train_logistic_regression], test_model, use_pca=True)

Svm test accuracy: 0.9680
Logistic Regression test accuracy: 0.9800

('Logistic Regression', 0.98)

# Find the highest accuracy model for HCC1806, training on non-reduced Drop-seq data and testing on Smart-seq
select_best_cross_dataset_model(ds_hcc_norm, ss_hcc_norm,
    [train_svm, train_logistic_regression], test_model, use_pca=False)

Svm test accuracy: 0.7912

Logistic Regression test accuracy: 0.7857

('Svm', 0.7912087912087912)

# With PCA reduced data
select_best_cross_dataset_model(ds_hcc_norm, ss_hcc_norm,
    [train_svm, train_logistic_regression], test_model, use_pca=True)

Svm test accuracy: 0.7198
Logistic Regression test accuracy: 0.6978

('Svm', 0.7197802197802198)

# Note precision & recall
model, *_ = train_cross_dataset_classifier(
    ds_hcc_norm, ss_hcc_norm,
    model_func=train_svm,
    test_func=test_model,
)

========================= Training =========================
Best Parameters: {'C': 0.025}
Best Score (CV avg): 0.8780823960759975
C: 0.025
Penalty: l2
Intercept: [-0.15301313]
Max Iterations: 10000
Number of iterations for convergence: 7
Training accuracy: 0.8914997956681651
========================= Testing =========================
Confusion matrix:
             Predicted Hypo  Predicted Norm
Actual Hypo              96               1
Actual Norm              37              48
Accuracy: 0.7912087912087912
Classification report:
               precision    recall  f1-score   support

        Hypo       0.72      0.99      0.83        97
        Norm       0.98      0.56      0.72        85

    accuracy                           0.79       182
   macro avg       0.85      0.78      0.78       182
weighted avg       0.84      0.79      0.78       182

# Find the highest accuracy model for Smart-seq using non-reduced data
select_best_cross_dataset_model(ss_hcc_norm, ss_mcf7_norm,
    [train_svm, train_logistic_regression, train_random_forest], test_model, use_pca=False)

Svm test accuracy: 0.9200
Logistic Regression test accuracy: 0.9240
Random Forest test accuracy: 0.8880

('Logistic Regression', 0.924)

# With PCA reduced data
select_best_cross_dataset_model(ss_hcc_norm, ss_mcf7_norm,
    [train_svm, train_logistic_regression, train_random_forest], test_model, use_pca=True)

Svm test accuracy: 0.8280
Logistic Regression test accuracy: 0.9560
Random Forest test accuracy: 0.9920

('Random Forest', 0.992)

# Find the highest accuracy model for Drop-seq using non-reduced data
select_best_cross_dataset_model(ds_hcc_norm, ds_mcf7_norm,
    [train_svm, train_logistic_regression], test_model, use_pca=False)

Svm test accuracy: 0.6995
Logistic Regression test accuracy: 0.7066

('Logistic Regression', 0.7065569222232498)

# With PCA reduced data
select_best_cross_dataset_model(ds_hcc_norm, ds_mcf7_norm,
    [train_svm, train_logistic_regression, train_random_forest], test_model, use_pca=True)

Svm test accuracy: 0.7030
Logistic Regression test accuracy: 0.7040
Random Forest test accuracy: 0.6643

('Logistic Regression', 0.7039674465920651)

# Note precision & recall
model, *_ = train_cross_dataset_classifier(
    ds_hcc_norm, ds_mcf7_norm,
    model_func=train_svm,
    test_func=test_model,
)

========================= Training =========================
Best Parameters: {'C': 0.025}
Best Score (CV avg): 0.9119331344241793
C: 0.025
Penalty: l2
Intercept: [-0.28561926]
Max Iterations: 10000
Number of iterations for convergence: 7
Training accuracy: 0.9298460700177088
========================= Testing =========================
Confusion matrix:
             Predicted Hypo  Predicted Norm
Actual Hypo            7063            1858
Actual Norm            4640            8065
Accuracy: 0.6995283455100342
Classification report:
               precision    recall  f1-score   support

        Hypo       0.60      0.79      0.68      8921
        Norm       0.81      0.63      0.71     12705

    accuracy                           0.70     21626
   macro avg       0.71      0.71      0.70     21626
weighted avg       0.73      0.70      0.70     21626

Description	Variable Name
Smart-seq unfiltered MCF7	`ss_mcf7_raw`
Smart-seq filtered MCF7	`ss_mcf7_filt`
Smart-seq filtered + normalized MCF7	`ss_mcf7_norm`
Smart-seq unfiltered HCC1806	`ss_hcc_raw`
Smart-seq filtered + normalized HCC1806	`ss_hcc_norm`
Drop-seq filtered MCF7	`ds_mcf7_filt`
Drop-seq filtered + normalized MCF7	`ds_mcf7_norm`
Drop-seq filtered HCC1806	`ds_hcc_filt`
Drop-seq filtered + normalized HCC1806	`ds_hcc_norm`

	Cell Line	Lane	Pos	Condition	Hours	Cell name	PreprocessingTag	ProcessingComments
Filename
output.STAR.1_A10_Hypo_S28_Aligned.sortedByCoord.out.bam	MCF7	output.STAR.1	A10	Hypo	72	S28	Aligned.sortedByCoord.out.bam	STAR,FeatureCounts
output.STAR.1_A11_Hypo_S29_Aligned.sortedByCoord.out.bam	MCF7	output.STAR.1	A11	Hypo	72	S29	Aligned.sortedByCoord.out.bam	STAR,FeatureCounts
output.STAR.1_A12_Hypo_S30_Aligned.sortedByCoord.out.bam	MCF7	output.STAR.1	A12	Hypo	72	S30	Aligned.sortedByCoord.out.bam	STAR,FeatureCounts
output.STAR.1_A1_Norm_S1_Aligned.sortedByCoord.out.bam	MCF7	output.STAR.1	A1	Norm	72	S1	Aligned.sortedByCoord.out.bam	STAR,FeatureCounts
output.STAR.1_A2_Norm_S2_Aligned.sortedByCoord.out.bam	MCF7	output.STAR.1	A2	Norm	72	S2	Aligned.sortedByCoord.out.bam	STAR,FeatureCounts

	output.STAR.1_A10_Hypo_S28_Aligned.sortedByCoord.out.bam	output.STAR.1_A3_Norm_S3_Aligned.sortedByCoord.out.bam	output.STAR.1_A4_Norm_S4_Aligned.sortedByCoord.out.bam	output.STAR.1_A5_Norm_S5_Aligned.sortedByCoord.out.bam	...	output.STAR.4_H14_Hypo_S383_Aligned.sortedByCoord.out.bam	output.STAR.4_H1_Norm_S355_Aligned.sortedByCoord.out.bam	output.STAR.4_H2_Norm_S356_Aligned.sortedByCoord.out.bam	output.STAR.4_H3_Norm_S357_Aligned.sortedByCoord.out.bam	output.STAR.4_H6_Norm_S360_Aligned.sortedByCoord.out.bam	output.STAR.4_H7_Hypo_S379_Aligned.sortedByCoord.out.bam	output.STAR.4_H8_Hypo_S380_Aligned.sortedByCoord.out.bam	output.STAR.4_H9_Hypo_S381_Aligned.sortedByCoord.out.bam
WASH7P	0	0	0	0	...	0	0	0	1	0	1	0	1
MIR6859-1	0	0	0	0	...	0	0	0	0	0	0	0	0
WASH9P	1	1	10	1	...	1	1	0	0	1	1	4	5
OR4F29	0	0	0	0	...	0	0	0	0	0	0	1	0
MTND1P23	0	0	0	0	...	0	0	1	0	0	0	0	0

	output.STAR.1_A10_Hypo_S28_Aligned.sortedByCoord.out.bam	output.STAR.1_A11_Hypo_S29_Aligned.sortedByCoord.out.bam	output.STAR.1_A12_Hypo_S30_Aligned.sortedByCoord.out.bam	output.STAR.1_A1_Norm_S1_Aligned.sortedByCoord.out.bam	output.STAR.1_A2_Norm_S2_Aligned.sortedByCoord.out.bam	output.STAR.1_A3_Norm_S3_Aligned.sortedByCoord.out.bam	output.STAR.1_A4_Norm_S4_Aligned.sortedByCoord.out.bam	output.STAR.1_A5_Norm_S5_Aligned.sortedByCoord.out.bam	output.STAR.1_A6_Norm_S6_Aligned.sortedByCoord.out.bam	output.STAR.1_A7_Hypo_S25_Aligned.sortedByCoord.out.bam	...	output.STAR.4_H14_Hypo_S383_Aligned.sortedByCoord.out.bam	output.STAR.4_H1_Norm_S355_Aligned.sortedByCoord.out.bam	output.STAR.4_H2_Norm_S356_Aligned.sortedByCoord.out.bam	output.STAR.4_H3_Norm_S357_Aligned.sortedByCoord.out.bam	output.STAR.4_H4_Norm_S358_Aligned.sortedByCoord.out.bam	output.STAR.4_H5_Norm_S359_Aligned.sortedByCoord.out.bam	output.STAR.4_H6_Norm_S360_Aligned.sortedByCoord.out.bam	output.STAR.4_H7_Hypo_S379_Aligned.sortedByCoord.out.bam	output.STAR.4_H8_Hypo_S380_Aligned.sortedByCoord.out.bam	output.STAR.4_H9_Hypo_S381_Aligned.sortedByCoord.out.bam
count	22934.000000	22934.000000	22934.000000	22934.000000	22934.000000	22934.000000	22934.000000	22934.000000	22934.000000	22934.000000	...	22934.000000	22934.000000	22934.000000	22934.000000	22934.000000	22934.000000	22934.000000	22934.000000	22934.000000	22934.000000
mean	40.817651	0.012253	86.442400	1.024636	14.531351	56.213613	75.397183	62.767725	67.396747	2.240734	...	17.362562	42.080230	34.692422	32.735284	21.992718	17.439391	49.242784	61.545609	68.289352	62.851400
std	465.709940	0.207726	1036.572689	6.097362	123.800530	503.599145	430.471519	520.167576	459.689019	25.449630	...	193.153757	256.775704	679.960908	300.291051	153.441647	198.179666	359.337479	540.847355	636.892085	785.670341
min	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
50%	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
75%	17.000000	0.000000	5.000000	0.000000	7.000000	23.000000	39.000000	35.000000	38.000000	1.000000	...	9.000000	30.000000	0.000000	17.000000	12.000000	9.000000	27.000000	30.000000	38.000000	33.000000
max	46744.000000	14.000000	82047.000000	289.000000	10582.000000	46856.000000	29534.000000	50972.000000	36236.000000	1707.000000	...	17800.000000	23355.000000	81952.000000	29540.000000	12149.000000	19285.000000	28021.000000	40708.000000	46261.000000	68790.000000

	output.STAR.PCRPlate1A10_Normoxia_S123_Aligned.sortedByCoord.out.bam	output.STAR.PCRPlate1A4_Hypoxia_S8_Aligned.sortedByCoord.out.bam	output.STAR.PCRPlate1A5_Hypoxia_S108_Aligned.sortedByCoord.out.bam	output.STAR.PCRPlate1A7_Normoxia_S113_Aligned.sortedByCoord.out.bam	...	output.STAR.PCRPlate4H10_Normoxia_S210_Aligned.sortedByCoord.out.bam	output.STAR.PCRPlate4H2_Hypoxia_S199_Aligned.sortedByCoord.out.bam
WASH7P	0	0	0	0	...	0	0
CICP27	0	0	0	0	...	0	0
DDX11L17	0	1	0	0	...	0	0
WASH9P	0	0	0	1	...	1	1
OR4F29	2	0	1	0	...	0	0

	output.STAR.PCRPlate1A10_Normoxia_S123_Aligned.sortedByCoord.out.bam	output.STAR.PCRPlate1A12_Normoxia_S26_Aligned.sortedByCoord.out.bam	output.STAR.PCRPlate1A1_Hypoxia_S97_Aligned.sortedByCoord.out.bam	output.STAR.PCRPlate1A2_Hypoxia_S104_Aligned.sortedByCoord.out.bam	output.STAR.PCRPlate1A3_Hypoxia_S4_Aligned.sortedByCoord.out.bam	output.STAR.PCRPlate1A4_Hypoxia_S8_Aligned.sortedByCoord.out.bam	output.STAR.PCRPlate1A5_Hypoxia_S108_Aligned.sortedByCoord.out.bam	output.STAR.PCRPlate1A6_Hypoxia_S11_Aligned.sortedByCoord.out.bam	output.STAR.PCRPlate1A7_Normoxia_S113_Aligned.sortedByCoord.out.bam	output.STAR.PCRPlate1A8_Normoxia_S119_Aligned.sortedByCoord.out.bam	...	output.STAR.PCRPlate4G12_Normoxia_S243_Aligned.sortedByCoord.out.bam	output.STAR.PCRPlate4G1_Hypoxia_S193_Aligned.sortedByCoord.out.bam	output.STAR.PCRPlate4G2_Hypoxia_S198_Aligned.sortedByCoord.out.bam	output.STAR.PCRPlate4G6_Hypoxia_S232_Aligned.sortedByCoord.out.bam	output.STAR.PCRPlate4G7_Normoxia_S204_Aligned.sortedByCoord.out.bam	output.STAR.PCRPlate4H10_Normoxia_S210_Aligned.sortedByCoord.out.bam	output.STAR.PCRPlate4H11_Normoxia_S214_Aligned.sortedByCoord.out.bam	output.STAR.PCRPlate4H2_Hypoxia_S199_Aligned.sortedByCoord.out.bam	output.STAR.PCRPlate4H7_Normoxia_S205_Aligned.sortedByCoord.out.bam	output.STAR.PCRPlate4H9_Normoxia_S236_Aligned.sortedByCoord.out.bam
count	23396.000000	23396.000000	23396.000000	23396.000000	23396.000000	23396.000000	23396.000000	23396.000000	23396.000000	23396.000000	...	23396.000000	23396.000000	23396.000000	23396.000000	23396.000000	23396.000000	23396.000000	23396.000000	23396.000000	23396.000000
mean	99.565695	207.678278	9.694734	150.689007	35.700504	47.088434	152.799453	135.869422	38.363908	45.512139	...	76.361771	105.566593	54.026116	29.763806	28.905411	104.740725	35.181569	108.197940	37.279962	76.303855
std	529.532443	981.107905	65.546050	976.936548	205.885369	545.367706	864.974182	870.729740	265.062493	366.704721	...	346.659348	536.881574	344.068304	186.721266	135.474736	444.773045	170.872090	589.082268	181.398951	369.090274
min	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
50%	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	1.000000	0.000000	0.000000	0.000000	0.000000	2.000000	0.000000	0.000000	0.000000	1.000000
75%	51.000000	125.000000	5.000000	40.000000	22.000000	17.000000	81.000000	76.000000	22.000000	18.000000	...	56.000000	67.000000	29.000000	18.000000	19.000000	76.000000	24.000000	68.000000	22.000000	44.000000
max	35477.000000	69068.000000	6351.000000	70206.000000	17326.000000	47442.000000	43081.000000	62813.000000	30240.000000	35450.000000	...	19629.000000	30987.000000	21894.000000	13457.000000	11488.000000	33462.000000	15403.000000	34478.000000	10921.000000	28532.000000

	output.STAR.1_A10_Hypo_S28_Aligned.sortedByCoord.out.bam	output.STAR.1_A12_Hypo_S30_Aligned.sortedByCoord.out.bam	output.STAR.1_A2_Norm_S2_Aligned.sortedByCoord.out.bam	output.STAR.1_A3_Norm_S3_Aligned.sortedByCoord.out.bam	output.STAR.1_A4_Norm_S4_Aligned.sortedByCoord.out.bam	output.STAR.1_A5_Norm_S5_Aligned.sortedByCoord.out.bam	output.STAR.1_A6_Norm_S6_Aligned.sortedByCoord.out.bam	output.STAR.1_B11_Hypo_S77_Aligned.sortedByCoord.out.bam	output.STAR.1_B12_Hypo_S78_Aligned.sortedByCoord.out.bam	output.STAR.1_B4_Norm_S52_Aligned.sortedByCoord.out.bam	...	output.STAR.4_H10_Hypo_S382_Aligned.sortedByCoord.out.bam	output.STAR.4_H14_Hypo_S383_Aligned.sortedByCoord.out.bam	output.STAR.4_H1_Norm_S355_Aligned.sortedByCoord.out.bam	output.STAR.4_H3_Norm_S357_Aligned.sortedByCoord.out.bam	output.STAR.4_H4_Norm_S358_Aligned.sortedByCoord.out.bam	output.STAR.4_H5_Norm_S359_Aligned.sortedByCoord.out.bam	output.STAR.4_H6_Norm_S360_Aligned.sortedByCoord.out.bam	output.STAR.4_H7_Hypo_S379_Aligned.sortedByCoord.out.bam	output.STAR.4_H8_Hypo_S380_Aligned.sortedByCoord.out.bam	output.STAR.4_H9_Hypo_S381_Aligned.sortedByCoord.out.bam
count	18945.000000	18945.000000	18945.000000	18945.000000	18945.000000	18945.000000	18945.000000	18945.000000	18945.000000	18945.000000	...	18945.000000	18945.000000	18945.000000	18945.000000	18945.000000	18945.000000	18945.000000	18945.000000	18945.000000	18945.000000
mean	49.409290	104.620639	17.589707	68.045395	91.260333	75.979784	81.576194	85.303985	49.655529	16.382792	...	54.711375	21.016785	50.920137	39.622486	26.620164	21.099023	59.585537	74.487305	82.655054	76.081499
std	511.986757	1139.662971	136.014975	553.362211	472.099720	571.441098	504.632248	911.153373	406.561440	160.981562	...	633.970615	212.338278	281.722199	329.984580	168.460343	217.871697	394.584632	594.260858	699.898130	863.857880
min	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
50%	0.000000	0.000000	1.000000	1.000000	4.000000	6.000000	4.000000	1.000000	3.000000	0.000000	...	0.000000	1.000000	4.000000	1.000000	0.000000	1.000000	0.000000	0.000000	1.000000	1.000000
75%	28.000000	27.000000	10.000000	36.000000	57.000000	52.000000	55.000000	48.000000	33.000000	9.000000	...	28.000000	13.000000	42.000000	25.000000	17.000000	13.000000	42.000000	45.000000	55.000000	48.000000
max	46744.000000	82047.000000	10582.000000	46856.000000	29534.000000	50972.000000	36236.000000	56068.000000	24994.000000	13587.000000	...	49147.000000	17800.000000	23355.000000	29540.000000	12149.000000	19285.000000	28021.000000	40708.000000	46261.000000	68790.000000

	output.STAR.PCRPlate1A10_Normoxia_S123_Aligned.sortedByCoord.out.bam	output.STAR.PCRPlate1A12_Normoxia_S26_Aligned.sortedByCoord.out.bam	output.STAR.PCRPlate1A2_Hypoxia_S104_Aligned.sortedByCoord.out.bam	output.STAR.PCRPlate1A3_Hypoxia_S4_Aligned.sortedByCoord.out.bam	output.STAR.PCRPlate1A4_Hypoxia_S8_Aligned.sortedByCoord.out.bam	output.STAR.PCRPlate1A5_Hypoxia_S108_Aligned.sortedByCoord.out.bam	output.STAR.PCRPlate1A6_Hypoxia_S11_Aligned.sortedByCoord.out.bam	output.STAR.PCRPlate1A7_Normoxia_S113_Aligned.sortedByCoord.out.bam	output.STAR.PCRPlate1A8_Normoxia_S119_Aligned.sortedByCoord.out.bam	output.STAR.PCRPlate1A9_Normoxia_S20_Aligned.sortedByCoord.out.bam	...	output.STAR.PCRPlate4G12_Normoxia_S243_Aligned.sortedByCoord.out.bam	output.STAR.PCRPlate4G1_Hypoxia_S193_Aligned.sortedByCoord.out.bam	output.STAR.PCRPlate4G2_Hypoxia_S198_Aligned.sortedByCoord.out.bam	output.STAR.PCRPlate4G6_Hypoxia_S232_Aligned.sortedByCoord.out.bam	output.STAR.PCRPlate4G7_Normoxia_S204_Aligned.sortedByCoord.out.bam	output.STAR.PCRPlate4H10_Normoxia_S210_Aligned.sortedByCoord.out.bam	output.STAR.PCRPlate4H11_Normoxia_S214_Aligned.sortedByCoord.out.bam	output.STAR.PCRPlate4H2_Hypoxia_S199_Aligned.sortedByCoord.out.bam	output.STAR.PCRPlate4H7_Normoxia_S205_Aligned.sortedByCoord.out.bam	output.STAR.PCRPlate4H9_Normoxia_S236_Aligned.sortedByCoord.out.bam
count	19503.000000	19503.000000	19503.000000	19503.000000	19503.000000	19503.000000	19503.000000	19503.000000	19503.000000	19503.000000	...	19503.000000	19503.000000	19503.000000	19503.000000	19503.000000	19503.000000	19503.000000	19503.000000	19503.000000	19503.000000
mean	119.427883	249.107522	180.739527	42.818233	56.444393	183.264677	162.976670	46.014305	54.589961	96.803210	...	91.583090	126.622930	64.801005	35.702302	34.670461	125.629544	42.195457	129.769010	44.715941	91.517561
std	577.934133	1069.768525	1067.470509	224.823960	596.882811	944.432350	951.367277	289.708746	401.024242	487.943421	...	377.847391	585.760835	375.921207	203.991666	147.706909	484.448028	186.359651	643.033801	197.842998	402.529704
min	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
50%	3.000000	7.000000	0.000000	1.000000	0.000000	0.000000	4.000000	0.000000	0.000000	9.000000	...	9.000000	4.000000	1.000000	2.000000	1.000000	15.000000	3.000000	4.000000	2.000000	8.000000
75%	75.000000	179.000000	111.000000	31.000000	29.000000	126.000000	106.000000	32.000000	29.000000	78.000000	...	77.000000	94.000000	42.000000	25.000000	27.000000	105.000000	34.000000	94.000000	32.000000	63.000000
max	35477.000000	69068.000000	70206.000000	17326.000000	47442.000000	43081.000000	62813.000000	30240.000000	35450.000000	42310.000000	...	19629.000000	30987.000000	21894.000000	13457.000000	11488.000000	33462.000000	15403.000000	34478.000000	10921.000000	28532.000000

	output.STAR.2_B3_Norm_S57_Aligned.sortedByCoord.out.bam	output.STAR.2_B4_Norm_S58_Aligned.sortedByCoord.out.bam	output.STAR.2_B5_Norm_S59_Aligned.sortedByCoord.out.bam	output.STAR.2_B6_Norm_S60_Aligned.sortedByCoord.out.bam	output.STAR.2_B7_Hypo_S79_Aligned.sortedByCoord.out.bam	output.STAR.2_B9_Hypo_S81_Aligned.sortedByCoord.out.bam	output.STAR.2_C10_Hypo_S130_Aligned.sortedByCoord.out.bam	output.STAR.2_C11_Hypo_S131_Aligned.sortedByCoord.out.bam	output.STAR.2_C1_Norm_S103_Aligned.sortedByCoord.out.bam	output.STAR.2_C2_Norm_S104_Aligned.sortedByCoord.out.bam	...	output.STAR.4_H10_Hypo_S382_Aligned.sortedByCoord.out.bam	output.STAR.4_H14_Hypo_S383_Aligned.sortedByCoord.out.bam	output.STAR.4_H1_Norm_S355_Aligned.sortedByCoord.out.bam	output.STAR.4_H3_Norm_S357_Aligned.sortedByCoord.out.bam	output.STAR.4_H4_Norm_S358_Aligned.sortedByCoord.out.bam	output.STAR.4_H5_Norm_S359_Aligned.sortedByCoord.out.bam	output.STAR.4_H6_Norm_S360_Aligned.sortedByCoord.out.bam	output.STAR.4_H7_Hypo_S379_Aligned.sortedByCoord.out.bam	output.STAR.4_H8_Hypo_S380_Aligned.sortedByCoord.out.bam	output.STAR.4_H9_Hypo_S381_Aligned.sortedByCoord.out.bam
count	3000.000000	3000.000000	3000.000000	3000.000000	3000.000000	3000.000000	3000.000000	3000.000000	3000.000000	3000.000000	...	3000.000000	3000.000000	3000.000000	3000.000000	3000.000000	3000.000000	3000.000000	3000.000000	3000.000000	3000.000000
mean	74.140333	90.907000	99.089000	88.137000	110.395667	148.849000	126.422667	142.229667	91.781000	91.426333	...	144.008333	133.846000	98.699333	84.070333	101.416333	96.636667	92.344333	154.387333	125.340000	132.017667
std	345.005307	409.560228	442.980702	425.804372	822.178446	1710.088769	1351.567001	1515.496440	388.660906	376.793214	...	1349.125183	1242.320764	417.410827	406.100983	513.988262	499.224863	680.698856	1169.686762	1066.926126	1422.143351
min	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
50%	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
75%	24.000000	37.000000	33.000000	34.000000	38.250000	24.000000	13.000000	22.000000	37.000000	44.000000	...	33.000000	38.000000	52.250000	25.000000	33.000000	44.000000	17.000000	19.000000	21.000000	20.250000
max	8222.000000	10167.000000	11446.000000	10312.000000	30586.000000	65037.000000	52680.000000	60789.000000	9394.000000	9077.000000	...	56392.000000	50404.000000	11352.000000	8713.000000	17006.000000	16625.000000	29663.000000	34565.000000	34175.000000	57814.000000

	output.STAR.2_B3_Norm_S57_Aligned.sortedByCoord.out.bam	output.STAR.2_B4_Norm_S58_Aligned.sortedByCoord.out.bam	output.STAR.2_B5_Norm_S59_Aligned.sortedByCoord.out.bam	output.STAR.2_B6_Norm_S60_Aligned.sortedByCoord.out.bam	output.STAR.2_B7_Hypo_S79_Aligned.sortedByCoord.out.bam	output.STAR.2_B9_Hypo_S81_Aligned.sortedByCoord.out.bam	output.STAR.2_C10_Hypo_S130_Aligned.sortedByCoord.out.bam	output.STAR.2_C11_Hypo_S131_Aligned.sortedByCoord.out.bam	output.STAR.2_C1_Norm_S103_Aligned.sortedByCoord.out.bam	output.STAR.2_C2_Norm_S104_Aligned.sortedByCoord.out.bam	...	output.STAR.4_H10_Hypo_S382_Aligned.sortedByCoord.out.bam	output.STAR.4_H14_Hypo_S383_Aligned.sortedByCoord.out.bam	output.STAR.4_H1_Norm_S355_Aligned.sortedByCoord.out.bam	output.STAR.4_H3_Norm_S357_Aligned.sortedByCoord.out.bam	output.STAR.4_H4_Norm_S358_Aligned.sortedByCoord.out.bam	output.STAR.4_H5_Norm_S359_Aligned.sortedByCoord.out.bam	output.STAR.4_H6_Norm_S360_Aligned.sortedByCoord.out.bam	output.STAR.4_H7_Hypo_S379_Aligned.sortedByCoord.out.bam	output.STAR.4_H8_Hypo_S380_Aligned.sortedByCoord.out.bam	output.STAR.4_H9_Hypo_S381_Aligned.sortedByCoord.out.bam
CYP1B1	343	131	452	27	5817	3841	9263	21543	1013	53	...	7890	4512	160	351	327	196	504	34565	20024	5953
CYP1B1-AS1	140	59	203	7	2669	1565	3866	9113	459	22	...	3647	2035	75	138	130	102	238	13717	7835	2367
CYP1A1	0	0	0	0	0	79	238	443	0	0	...	86	1654	0	0	0	1	0	11274	563	522
NDRG1	0	1	0	0	654	1263	2634	540	0	13	...	481	1052	0	0	54	243	62	1263	925	1572
DDIT4	386	289	0	288	2484	2596	1323	2044	36	204	...	3692	2410	800	1	189	266	417	4256	12733	2275

	total_counts	n_genes
count	6.300000e+01	63.000000
mean	1.104397e+06	9660.587302
std	5.121923e+05	1192.260534
min	2.633430e+05	6433.000000
25%	7.101980e+05	8902.500000
50%	1.229842e+06	9791.000000
75%	1.515264e+06	10590.500000
max	1.982038e+06	11780.000000

	total_counts	n_genes
count	2.500000e+02	250.000000
mean	1.171277e+06	10099.304000
std	3.613786e+05	1253.815198
min	2.847180e+05	5322.000000
25%	9.383340e+05	9443.250000
50%	1.198204e+06	10303.000000
75%	1.448138e+06	10955.250000
max	1.970851e+06	12217.000000

	total_counts	n_genes
count	4.500000e+01	45.000000
mean	2.495615e+06	10189.177778
std	1.176204e+06	1117.962641
min	6.968360e+05	7558.000000
25%	1.100835e+06	9416.000000
50%	2.853875e+06	10354.000000
75%	3.351452e+06	10996.000000
max	4.858344e+06	11830.000000

	total_counts	n_genes
count	1.820000e+02	182.000000
mean	1.996438e+06	10809.527473
std	1.040057e+06	957.956139
min	3.421010e+05	7268.000000
25%	1.001764e+06	10334.000000
50%	1.974784e+06	10907.500000
75%	2.791580e+06	11498.250000
max	4.774799e+06	12629.000000

	AAAAACCTATCG_Normoxia	AAAACAACCCTA_Normoxia	AAAACACTCTCA_Normoxia	AAAACCAGGCAC_Normoxia	AAAACCTAGCTC_Normoxia	AAAACCTCCGGG_Normoxia	AAAACTCGTTGC_Normoxia	AAAAGAGCTCTC_Normoxia	AAAAGCTAGGCG_Normoxia	AAAATCGCATTT_Normoxia	...	TTTTACAGGATC_Hypoxia	TTTTACCACGTA_Hypoxia	TTTTATGCTACG_Hypoxia	TTTTCGCGCTCG_Hypoxia	TTTTCGCGTAGA_Hypoxia	TTTTCGTCCGCT_Hypoxia	TTTTCTCCGGCT_Hypoxia	TTTTGTTCAAAG_Hypoxia	TTTTTTGTATGT_Hypoxia
MALAT1	1	3	3	6	4	5	1	13	3	3	...	0	2	1	1	0	1	0	0	4
MT-RNR2	0	0	0	2	0	0	2	1	7	0	...	0	0	0	0	0	0	0	0	0
NEAT1	0	0	0	0	0	2	0	1	2	0	...	0	0	0	0	0	0	0	0	0
H1-5	0	0	0	0	0	2	0	0	0	0	...	0	1	0	1	0	0	1	0	0
TFF1	4	1	1	1	0	0	0	2	0	1	...	2	3	8	0	3	4	2	6	0

	AAAAAACCCGGC_Normoxia	AAAACCGGATGC_Normoxia	AAAACGAGCTAG_Normoxia	AAAACTTCCCCG_Normoxia	AAAAGCCTACCC_Normoxia	AAACACAAATCT_Normoxia	AAACCAAGCCCA_Normoxia	AAACCATGCACT_Normoxia	AAACCTCCGGCT_Normoxia	AAACGCCGGTCC_Normoxia	...	TTTTCTGATGGT_Hypoxia	TTTTGATTCAGA_Hypoxia	TTTTGCAACTGA_Hypoxia	TTTTGCCGGGCC_Hypoxia	TTTTGTTAGCCT_Hypoxia	TTTTTCCGTGCA_Hypoxia	TTTTTGCCTGGG_Hypoxia	TTTTTGTAACAG_Hypoxia	TTTTTTTGAATC_Hypoxia
H1-5	2	2	5	1	0	0	0	0	1	0	...	0	1	0	2	1	0	0	3	1
MALAT1	3	3	2	3	12	3	1	2	0	0	...	3	1	1	1	4	4	1	3	6
MT-RNR2	0	0	0	0	0	0	0	0	0	1	...	1	2	2	2	0	1	0	1	0
ARVCF	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0
BCYRN1	0	1	1	0	0	1	1	2	0	3	...	1	1	0	1	1	0	1	0	0

	AAAAAACCCGGC_Normoxia	AAAACCGGATGC_Normoxia	AAAACGAGCTAG_Normoxia	AAAACTTCCCCG_Normoxia	AAAAGCCTACCC_Normoxia	AAACACAAATCT_Normoxia	AAACCAAGCCCA_Normoxia	AAACCATGCACT_Normoxia	AAACCTCCGGCT_Normoxia	AAACGCCGGTCC_Normoxia	...	TTTTCTGATGGT_Hypoxia	TTTTGATTCAGA_Hypoxia	TTTTGCAACTGA_Hypoxia	TTTTGCCGGGCC_Hypoxia	TTTTGTTAGCCT_Hypoxia	TTTTTACCAATC_Hypoxia	TTTTTCCGTGCA_Hypoxia	TTTTTGCCTGGG_Hypoxia	TTTTTGTAACAG_Hypoxia	TTTTTTTGAATC_Hypoxia
count	3000.00000	3000.000000	3000.000000	3000.000000	3000.000000	3000.000000	3000.000000	3000.00000	3000.000000	3000.000000	...	3000.000000	3000.000000	3000.000000	3000.000000	3000.000000	3000.000000	3000.000000	3000.000000	3000.000000	3000.000000
mean	0.02900	0.041667	0.024333	0.021667	0.029667	0.020000	0.036000	0.02600	0.034000	0.029333	...	0.043000	0.049667	0.037000	0.047667	0.057000	0.023333	0.041667	0.041667	0.043333	0.040000
std	0.23276	0.309778	0.231860	0.189409	0.323761	0.170126	0.250449	0.23525	0.231362	0.218683	...	0.271739	0.319219	0.279864	0.259648	0.304053	0.214797	0.236536	0.285116	0.267356	0.282418
min	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
50%	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
75%	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
max	5.00000	9.000000	7.000000	4.000000	12.000000	3.000000	4.000000	6.00000	4.000000	4.000000	...	4.000000	7.000000	7.000000	4.000000	5.000000	4.000000	4.000000	5.000000	5.000000	6.000000

	Dataset	Scaling	PCs for 95% var	Best PC pair	Accuracy
0	SmartSeq MCF7	Unscaled	20	1,6	0.992
1	SmartSeq MCF7	Scaled	204	1,2	0.992
2	SmartSeq HCC1806	Unscaled	34	2,3	0.945
3	SmartSeq HCC1806	Scaled	161	2,3	0.901
4	DropSeq MCF7	Unscaled	761	2,3	0.869
5	DropSeq MCF7	Scaled	2652	2,3	0.901
6	DropSeq HCC1806	Unscaled	844	5,6	0.809
7	DropSeq HCC1806	Scaled	2595	3,4	0.893

Introduction¶

Dataset Naming Convention¶

Components:¶

Examples:¶

Imports¶

Meta Data¶

Unfiltered SmartSeq MCF7¶

Exploration¶

Gene Counts¶

Outliers¶

Quality Control & Violin Plots¶

Duplicates¶

Skeweness & Kurtosis¶

Train a linear classifier in PCA space¶

Unfiltered SmartSeq HCC1806¶

Exploration¶

Gene Counts¶

Outliers¶

Quality Control & Violin Plots¶

Duplicates¶

Skeweness & Kurtosis¶

Train a linear classifier in PCA space¶

Differences between "...unfiltered...txt", "...filtered...txt", and "..normalized...txt" data¶

Data Overview¶

SmartSeq MCF7 Raw vs Filtered¶

Gene Filtering¶

Cell Filtering¶

SmartSeq MCF7 Filtered vs Normalised + Filtered¶

Exploration¶

What normalisation could have been applied?¶

Why 63 Cells Were Dropped?¶

Why 15945 Genes Were Dropped?¶

SmartSeq HCC1806 Raw vs Filtered¶

Gene Filtering¶

Cell Filtering¶

SmartSeq HCC1806 Filtered vs Normalised + Filtered¶

Exploration¶

Normalisation¶

Dropped vs Retained Cells¶

Cell Filtering Outcome Summary¶

Dropped Cells:¶

Retained Cells:¶

Dropped vs Retained Genes¶

DropSeq¶

Sequencing Technology Comparison¶

Label Distribution¶

Correlation between gene expression profiles¶

Unsupervised Learning¶

PCA¶

Helper functions¶

SmartSeq¶

SmartSeq MCF7¶

SmartSeq HCC1806¶

DropSeq¶

DropSeq MCF7¶

DropSeq HCC1806¶

DropSeq issue¶

Results summary¶

K-NN graph¶

functions¶

MCF7 Smart Seq¶

HCC1806 Smart Seq¶

MCF7 Drop Seq¶

HCC11806 Dropseq¶

t-SNE¶

functions¶

MCF7 Smart Seq¶

HCC1806 Smart Seq¶

MCF7 Drop-Seq¶

HCC Drop-Seq¶

UMAP¶

MCF7 SmartSeq

HCC SmartSeq¶

MCF7 DropSeq¶

HCC DropSeq¶

K-Means Clustering¶

helper functions¶

MCF7 Smart Seq¶

HCC Smart Seq¶

MCF7 Drop Seq¶